Merge pull request 'zsl' (#14) from zsl into master

Reviewed-on: #14

README.md

@@ -1,2 +1,8 @@
-1.dsj
-2.0811 
+1.2025.01.21把之前的tuoheng alg仓库代码重新开个仓库 (1)在config/service/dsp_test_service.yml里面添加参数，控制存储用的oss还是minio storage_source: 1 2.2025.02.06 (1)修改代码，把mqtt读取加入到系统中。config/service/dsp_test_service.yml，中添加mqtt_flag,决定是否启用。 (2)修改了minio情况下的，文件名命名方式。 3.2025.02.12 (1)增加了对alg算法开发的代码。可以通过配置文件config/service/dsp_test_service.yml中algSwitch: true，决定是否启用。
+
+4、2025.07.10 周树亮 - 增加人群计数，自研车牌模型，裸土覆盖3个场景
+
+5、江朝庆 -- 0715
+1）代码整理，删除冗余代码。
+2）增加requirements.txt,方便部署
+3) logs

common/Constant.py

@@ -16,7 +16,6 @@ success_progess = "1.0000"
 width = 1400
 
 COLOR = (
-    [0, 0, 255],
     [255, 0, 0],
     [211, 0, 148],
     [0, 127, 0],
@@ -35,7 +34,8 @@ COLOR = (
     [8, 101, 139],
     [171, 130, 255],
     [139, 112, 74],
-    [205, 205, 180])
+    [205, 205, 180],
+    [0, 0, 255],)
 
 ONLINE = "online"
 OFFLINE = "offline"

concurrency/FileUploadThread.py

@@ -3,52 +3,38 @@ from concurrent.futures import ThreadPoolExecutor
 from threading import Thread
 from time import sleep, time
 from traceback import format_exc
+import numpy as np
 
 from loguru import logger
 import cv2
-import requests
-import json
-import base64
 from entity.FeedBack import message_feedback
 from enums.ExceptionEnum import ExceptionType
+from enums.ModelTypeEnum import ModelType
 from exception.CustomerException import ServiceException
 from util.AliyunSdk import AliyunOssSdk
 from util.MinioSdk import MinioSdk
 from util import TimeUtils
 from enums.AnalysisStatusEnum import AnalysisStatus
-from util.PlotsUtils import draw_painting_joint
+from util.PlotsUtils import draw_painting_joint, draw_name_ocr, draw_name_crowd,draw_transparent_red_polygon
 from util.QueUtil import put_queue, get_no_block_queue, clear_queue
 import io
 from util.LocationUtils import locate_byMqtt
 
 
-
 class FileUpload(Thread):
-    __slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg', '_mqtt_list')
+    __slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')
 
     def __init__(self, *args):
         super().__init__()
-        self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type,self._mqtt_list = args
+        self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
         self._storage_source = self._context['service']['storage_source']
-        self._algStatus = False # 默认关闭
-        
-        # self._algStatus = True # 默认关闭
-        self._algSwitch = self._context['service']['algSwitch']  
-        
-        
+        self._algStatus = False  # 默认关闭
 
-        
-        self.server_ip =  self._context['llm']['server_ip']
-        self.server_port = self._context['llm']['server_port']
-        self.is_use_llm = self._context['llm']['is_use_llm']
-        self.cnt = self._context['llm']['cnt']
-        
-        
-        
-        
-        
-        #0521:
-        default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"    
+        # self._algStatus = True # 默认关闭
+        self._algSwitch = self._context['service']['algSwitch']
+
+        # 0521:
+        default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
         if default_enabled:
             print("执行默认程序（defaultEnabled=True）")
             self._algSwitch = True
@@ -58,73 +44,14 @@ class FileUpload(Thread):
             # 这里放非默认逻辑的代码
             self._algSwitch = False
 
+        print("---line46 :FileUploadThread.py---", self._algSwitch)
+
+
 # 如果任务是在线、离线处理，则用此类
 class ImageFileUpload(FileUpload):
     __slots__ = ()
 
-    def extract_answers(self, text):  # 提取大模型中的分点文本内容
-        lines = [line.strip() for line in text.split('.') if line.strip()]
-        answers = []
-        for i, line in enumerate(lines):
-            if i > 0:
-                if i < len(lines) - 1:
-                    answers.append(line[:-1])
-                else:
-                    answers.append(line)
-        return answers
-    
-    def get_llm_res(self, image, prompt,SERVER_IP,API_PORT):
-
-        # SERVER_IP = "192.168.10.11"
-        # API_URL = f"http://{SERVER_IP}:8000/generate"
-
-        SERVER_IP = SERVER_IP
-        
-        API_URL = f"http://{SERVER_IP}:{API_PORT}/generate"
-
-        # _, buffer = cv2.imencode('.jpg', image)
-        # is_success, buffer = cv2.imencode('.png', image)
-        is_success, buffer = cv2.imencode('.webp', image, [cv2.IMWRITE_WEBP_QUALITY, 100])
-        image_base64 = base64.b64encode(buffer).decode('utf-8')
-
-        conversation = {
-            "prompt": prompt,
-            "image_base64": image_base64,
-        }
-
-        try:
-            logger.info(f"正在向 {API_URL} 发送请求...")
-            response = requests.post(API_URL, json=conversation, timeout=60) # 设置超时
-
-            response.raise_for_status() # 如果状态码不是2xx，会抛出异常
-
-            resp = response.json()
-            result = resp.get("response")
-
-            logger.info("\n--- 请求成功 ---")
-            logger.info(f"原始Prompt: {prompt}")
-            logger.info(f"模型生成结果: {result}")
-
-        except requests.exceptions.RequestException as e:
-            logger.info(f"\n--- 请求失败 ---")
-            logger.info(f"发生错误: {e}")
-        except json.JSONDecodeError:
-            logger.info("\n--- 解析失败 ---")
-            logger.info(f"无法解析服务器返回的响应: {response.text}")
-
-        new_result = self.extract_answers(result)
-        print("###line116",new_result)
-        
-        
-        
-        
-        new_result = new_result[1]  # 获取第二点内容
-        # flag = "不" in result or ("没" in result and "没错" not in result) or "否" in result or "未" in result or "无" in result
-        flag = "不存在" in new_result or ("没" in new_result and "没错" not in new_result) or (
-                           "否" in new_result and "是否" not in new_result) or "未" in new_result or "无法" in new_result
-        return not flag, new_result
-
-    #@staticmethod
+    # @staticmethod
     def handle_image(self, frame_msg, frame_step):
         # (high_score_image["code"], all_frames, draw_config["font_config"])
         # high_score_image["code"][code][cls] = (frame, frame_index_list[i], cls_list)
@@ -138,25 +65,40 @@ class ImageFileUpload(FileUpload):
         模型编号：modeCode
         检测目标：detectTargetCode
         '''
-        print('*'*100,' mqtt_list:',len(self._mqtt_list))
-
+        aFrame = frame.copy()
+        igH, igW = aFrame.shape[0:2]
         model_info = []
+        mqttPares= det_xywh['mqttPares']
+        border = None
+        gps = [None, None]
+        camParas = None
+        if mqttPares is not None:
+            if mqttPares[0] == 1:
+                border = mqttPares[1]
+            elif mqttPares[0] == 0:
+                camParas = mqttPares[1]
+        if border is not None:
+            aFrame = draw_transparent_red_polygon(aFrame, np.array(border, np.int32), alpha=0.25)
+        det_xywh.pop('mqttPares')
         # 更加模型编码解析数据
         for code, det_list in det_xywh.items():
             if len(det_list) > 0:
                 for cls, target_list in det_list.items():
                     if len(target_list) > 0:
-                        aFrame = frame.copy()
                         for target in target_list:
-                            draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config, target[5])
-                            igH,igW = aFrame.shape[0:2]
-                        if len(self._mqtt_list)>=1:
-                            #camParas = self._mqtt_list[0]['data']
-                            camParas = self._mqtt_list[0]
-                            gps = locate_byMqtt(target[1],igW,igH,camParas,outFormat='wgs84')
-                        else:
-                            gps=[None,None]
-                        model_info.append({"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame,'gps':gps})
+                            if camParas is not None:
+                                gps = locate_byMqtt(target[1], igW, igH, camParas, outFormat='wgs84')
+                            # 自研车牌模型判断
+                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
+                                draw_name_ocr(target[1], aFrame, target[4])
+                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                draw_name_crowd(target[1], aFrame, target[4])
+                            else:
+                                draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config,
+                                                    target[5],border)
+                        model_info.append(
+                            {"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame, 'gps': gps})
         if len(model_info) > 0:
             image_result = {
                 "or_frame": frame,
@@ -175,13 +117,15 @@ class ImageFileUpload(FileUpload):
         image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
         service_timeout = int(service["timeout"])
         frame_step = int(service["filter"]["frame_step"]) + 120
-        if msg['taskType']==0: self._algStatus = False
-        else: self._algStatus = True
+        if msg['taskType'] == 0:
+            self._algStatus = False
+        else:
+            self._algStatus = True
         try:
             with ThreadPoolExecutor(max_workers=2) as t:
                 # 初始化oss客户端
-                if self._storage_source==1:
-                    minioSdk = MinioSdk(base_dir, env, request_id )
+                if self._storage_source == 1:
+                    minioSdk = MinioSdk(base_dir, env, request_id)
                 else:
                     aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
                 start_time = time()
@@ -195,15 +139,16 @@ class ImageFileUpload(FileUpload):
                         # 获取队列中的消息
                         image_msg = get_no_block_queue(image_queue)
                         if image_msg is not None:
-                            
                             if image_msg[0] == 2:
-                                logger.info("图片上传线程收到命令:{}, requestId: {}",image_msg[1] ,request_id)
+                                logger.info("图片上传线程收到命令:{}, requestId: {}", image_msg[1], request_id)
                                 if 'stop' == image_msg[1]:
                                     logger.info("开始停止图片上传线程, requestId:{}", request_id)
                                     break
-                                if 'algStart' == image_msg[1]: self._algStatus = True; logger.info("图片上传线程,执行算法开启命令, requestId:{}", request_id)
-                                if 'algStop' == image_msg[1]:  self._algStatus = False; logger.info("图片上传线程,执行算法关闭命令, requestId:{}", request_id) 
-                            
+                                if 'algStart' == image_msg[1]: self._algStatus = True; logger.info(
+                                    "图片上传线程,执行算法开启命令, requestId:{}", request_id)
+                                if 'algStop' == image_msg[1]:  self._algStatus = False; logger.info(
+                                    "图片上传线程,执行算法关闭命令, requestId:{}", request_id)
+
                             if image_msg[0] == 1:
                                 image_result = self.handle_image(image_msg[1], frame_step)
                                 if image_result is not None:
@@ -213,117 +158,54 @@ class ImageFileUpload(FileUpload):
                                                                      image_result["last_frame"],
                                                                      analyse_type,
                                                                      "OR", "0", "0", request_id)
-                                    if   self._storage_source==1: 
-                                        or_future = t.submit(minioSdk.put_object, or_image, or_image_name)    
+                                    if self._storage_source == 1:
+                                        or_future = t.submit(minioSdk.put_object, or_image, or_image_name)
                                     else:
                                         or_future = t.submit(aliyunOssSdk.put_object, or_image_name, or_image.tobytes())
                                     task.append(or_future)
                                     model_info_list = image_result["model_info"]
-                                    llm_flag_list = []
                                     msg_list = []
-                                    
-
                                     for model_info in model_info_list:
-                                        
-                                        fire_flag = model_info.get("modelCode")
-                                        print("###line229",fire_flag)
-                                        
-                                        if fire_flag == "008":
-                                        
-                                            aFrame = model_info["aFrame"]
-                                            # aFrame_rgb = cv2.cvtColor(aFrame, cv2.COLOR_BGR2RGB)
-
-                                            tar_cls = model_info["detectTargetCode"]
-                                            logger.info("目标类别：{}", tar_cls)
-
-                                            # 0715 ： 这块也不对，因为需要具体到目标才可以，否则只看0 1 ，是无法做到判断正确的火情处理，还有大量其他的部分
-                                            
-                                            # is_use_llm = True
-                                            if tar_cls == '0':
-                                                # tar = "明火"
-                                                tar = "2. 判断图中是否有明火。3. 如果有，识别火势的大小，比如：[无情况/小/中/大]"
-                                            elif tar_cls == '1':
-                                                # tar = "某种物质燃烧产生的烟雾"
-                                                tar = "2. 判断图中是否有烟雾。3. 如果有，识别烟雾的颜色，比如：[无烟雾/白/灰白/灰黑]"
-                                            else:
-                                                self.is_use_llm = False
-                                                
-                                            llm_flag = True
-                                            if self.is_use_llm:
-                                                # prompt = f"我看到图中有{tar}，请你判断是否正确。简要回答。"
-                                                prompt = f"分点进行输出：1. 简短描述图像中的场景。{tar}"
-                                                logger.info("检测到目标，大模型提示词：{}", prompt)
-                                                
-                                                
-                                                # llm_flag, llm_res = self.get_llm_res(aFrame, prompt)
-
-                                                
-                                                llm_flag, llm_res = self.get_llm_res(aFrame, prompt,self.server_ip,self.server_port)
-
-                                                llm_flag_list.append(llm_flag)
-                                                logger.info("检测到目标，大模型识别图中是否存在目标：{}, 大模型输出：{}", llm_flag, llm_res)
-
-                                                if llm_flag:
-                                                    logger.info("经大模型筛查，小模型识别正确!!!")
-                                                    # cv2.imwrite(f"/home/thsw2/wei/image_res/{self.cnt}_{tar_cls}_1.jpg", aFrame, [cv2.IMWRITE_JPEG_QUALITY, 80])
-                                                    # self.cnt = self.cnt + 1
-                                                else:
-                                                    logger.info("经大模型筛查，小模型识别错误!!!")
-                                                    # cv2.imwrite(f"/home/thsw2/wei/image_res/{self.cnt}_{tar_cls}_0.jpg", aFrame, [cv2.IMWRITE_JPEG_QUALITY, 80])
-                                                    # self.cnt = self.cnt + 1
-                                        else:
-                                            llm_flag_list.append(llm_flag)
-                                            
-                                            
-
-                                        ai_image = cv2.imencode(".jpg", aFrame)[1]
+                                        ai_image = cv2.imencode(".jpg", model_info["aFrame"])[1]
                                         ai_image_name = build_image_name(image_result["current_frame"],
-                                                                        image_result["last_frame"],
-                                                                        analyse_type,
-                                                                        "AI",
-                                                                        model_info["modelCode"],
-                                                                        model_info["detectTargetCode"],
-                                                                        request_id)
-                                        if llm_flag:  # 根据大模型结果判定是否上传
-                                            if self._storage_source==1: 
-                                                ai_future = t.submit(minioSdk.put_object, ai_image,
-                                                                ai_image_name)
-                                            else:
-                                                ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
-                                                                ai_image.tobytes())
-                                            
-                                            task.append(ai_future)
-                                    
-                                    # print("###line288",len(task))
+                                                                         image_result["last_frame"],
+                                                                         analyse_type,
+                                                                         "AI",
+                                                                         model_info["modelCode"],
+                                                                         model_info["detectTargetCode"],
+                                                                         request_id)
+                                        if self._storage_source == 1:
+                                            ai_future = t.submit(minioSdk.put_object, ai_image,
+                                                                 ai_image_name)
+                                        else:
+                                            ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
+                                                                 ai_image.tobytes())
 
-                                        #msg_list.append(message_feedback(request_id,
+                                        task.append(ai_future)
+                                        # msg_list.append(message_feedback(request_id,
                                         #                                 AnalysisStatus.RUNNING.value,
                                         #                                 analyse_type, "", "", "",
                                         #                                 or_image_name,
                                         #                                 ai_image_name,
                                         #                                 model_info['modelCode'],
                                         #                                 model_info['detectTargetCode']))
-  
                                     remote_image_list = []
                                     for tk in task:
-                                        remote_image_list.append( tk.result() )
+                                        remote_image_list.append(tk.result())
 
                                     for ii, model_info in enumerate(model_info_list):
-                                        
-                                        if llm_flag_list[ii]:
-                                            msg_list.append(  message_feedback(request_id,
-                                                                            AnalysisStatus.RUNNING.value,
-                                                                            analyse_type, "", "", "",
-                                                                            remote_image_list[0],
-                                                                            remote_image_list[ii+1],
-                                                                            model_info['modelCode'],
-                                                                            model_info['detectTargetCode'],
-                                                                            longitude=model_info['gps'][0],
-                                                                            latitude=model_info['gps'][1],
-                                                                            )  )
-                                            
-                                                                        
-                                    if (not self._algSwitch)  or  ( self._algStatus and  self._algSwitch):
+                                        msg_list.append(message_feedback(request_id,
+                                                                         AnalysisStatus.RUNNING.value,
+                                                                         analyse_type, "", "", "",
+                                                                         remote_image_list[0],
+                                                                         remote_image_list[ii + 1],
+                                                                         model_info['modelCode'],
+                                                                         model_info['detectTargetCode'],
+                                                                         longitude=model_info['gps'][0],
+                                                                         latitude=model_info['gps'][1],
+                                                                         ))
+
+                                    if (not self._algSwitch) or (self._algStatus and self._algSwitch):
                                         for msg in msg_list:
                                             put_queue(fb_queue, msg, timeout=2, is_ex=False)
                                     del task, msg_list
@@ -348,9 +230,9 @@ def build_image_name(*args):
     time_now = TimeUtils.now_date_to_str("%Y-%m-%d-%H-%M-%S")
     return "%s/%s_frame-%s-%s_type_%s-%s-%s-%s_%s.jpg" % (request_id, time_now, current_frame, last_frame,
                                                           random_num, mode_type, modeCode, target, image_type)
-                                                          
 
-#如果任务是图像处理，则用此类
+
+# 如果任务是图像处理，则用此类
 class ImageTypeImageFileUpload(Thread):
     __slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg')
 
@@ -358,6 +240,7 @@ class ImageTypeImageFileUpload(Thread):
         super().__init__()
         self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
         self._storage_source = self._context['service']['storage_source']
+
     @staticmethod
     def handle_image(det_xywh, copy_frame, font_config):
         """
@@ -377,12 +260,21 @@ class ImageTypeImageFileUpload(Thread):
                     if target_list is not None and len(target_list) > 0:
                         aiFrame = copy_frame.copy()
                         for target in target_list:
-                            draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
-                        model_info.append({
-                            "modelCode": str(code),
-                            "detectTargetCode": str(cls),
-                            "frame": aiFrame
-                        })
+                            # 自研车牌模型判断
+                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
+                                draw_name_ocr(target, aiFrame, font_config[cls])
+                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or \
+                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                draw_name_crowd(target, aiFrame, font_config[cls])
+                            else:
+                                draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
+
+                            model_info.append({
+                                "modelCode": str(code),
+                                "detectTargetCode": str(cls),
+                                "frame": aiFrame
+                            })
+
         if len(model_info) > 0:
             image_result = {
                 "or_frame": copy_frame,
@@ -402,11 +294,11 @@ class ImageTypeImageFileUpload(Thread):
         with ThreadPoolExecutor(max_workers=2) as t:
             try:
                 # 初始化oss客户端
-                if self._storage_source==1:
-                    minioSdk = MinioSdk(base_dir, env, request_id )
+                if self._storage_source == 1:
+                    minioSdk = MinioSdk(base_dir, env, request_id)
                 else:
                     aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
-                
+
                 start_time = time()
                 while True:
                     try:
@@ -427,15 +319,15 @@ class ImageTypeImageFileUpload(Thread):
                                 if det_xywh is None:
                                     ai_image_name = build_image_name(0, 0, analyse_type, "AI", result.get("modelCode"),
                                                                      result.get("type"), request_id)
-                                                                     
-                                    if   self._storage_source==1: 
-                                        ai_future = t.submit(minioSdk.put_object, copy_frame,ai_image_name)    
+
+                                    if self._storage_source == 1:
+                                        ai_future = t.submit(minioSdk.put_object, copy_frame, ai_image_name)
                                     else:
                                         ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, copy_frame)
-                                                                     
+
                                     task.append(ai_future)
                                     remote_names.append(ai_image_name)
-                                    #msg_list.append(message_feedback(request_id,
+                                    # msg_list.append(message_feedback(request_id,
                                     #                                 AnalysisStatus.RUNNING.value,
                                     #                                 analyse_type, "", "", "",
                                     #                                 image_url,
@@ -446,17 +338,17 @@ class ImageTypeImageFileUpload(Thread):
                                 else:
                                     image_result = self.handle_image(det_xywh, copy_frame, font_config)
                                     if image_result:
-                                        
+
                                         # 图片帧数编码
                                         if image_url is None:
                                             or_result, or_image = cv2.imencode(".jpg", image_result.get("or_frame"))
                                             image_url_0 = build_image_name(image_result.get("current_frame"),
-                                                                         image_result.get("last_frame"),
-                                                                         analyse_type,
-                                                                         "OR", "0", "O", request_id)
-                                                                         
-                                            if   self._storage_source==1: 
-                                                or_future = t.submit(minioSdk.put_object, or_image,image_url_0)    
+                                                                           image_result.get("last_frame"),
+                                                                           analyse_type,
+                                                                           "OR", "0", "O", request_id)
+
+                                            if self._storage_source == 1:
+                                                or_future = t.submit(minioSdk.put_object, or_image, image_url_0)
                                             else:
                                                 or_future = t.submit(aliyunOssSdk.put_object, image_url_0,
                                                                      or_image.tobytes())
@@ -472,14 +364,14 @@ class ImageTypeImageFileUpload(Thread):
                                                                              model_info.get("modelCode"),
                                                                              model_info.get("detectTargetCode"),
                                                                              request_id)
-                                            if   self._storage_source==1: 
-                                                ai_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)    
-                                            else:    
+                                            if self._storage_source == 1:
+                                                ai_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)
+                                            else:
                                                 ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
                                                                      ai_image.tobytes())
                                             task.append(ai_future)
                                             remote_names.append(ai_image_name)
-                                            #msg_list.append(message_feedback(request_id,
+                                            # msg_list.append(message_feedback(request_id,
                                             #                                 AnalysisStatus.RUNNING.value,
                                             #                                 analyse_type, "", "", "",
                                             #                                 image_url,
@@ -490,9 +382,8 @@ class ImageTypeImageFileUpload(Thread):
                                 remote_url_list = []
                                 for thread_result in task:
                                     remote_url_list.append(thread_result.result())
-                                    
-                                    
-                                #以下代码是为了获取图像上传后，返回的全路径地址  
+
+                                # 以下代码是为了获取图像上传后，返回的全路径地址
                                 if det_xywh is None:
                                     msg_list.append(message_feedback(request_id,
                                                                      AnalysisStatus.RUNNING.value,
@@ -505,12 +396,12 @@ class ImageTypeImageFileUpload(Thread):
                                 else:
                                     if image_result:
                                         if image_url is None:
-                                            for ii in range(len(remote_names)-1):
+                                            for ii in range(len(remote_names) - 1):
                                                 msg_list.append(message_feedback(request_id,
                                                                                  AnalysisStatus.RUNNING.value,
                                                                                  analyse_type, "", "", "",
                                                                                  remote_url_list[0],
-                                                                                 remote_url_list[1+ii],
+                                                                                 remote_url_list[1 + ii],
                                                                                  model_info.get('modelCode'),
                                                                                  model_info.get('detectTargetCode'),
                                                                                  analyse_results=result))
@@ -522,13 +413,10 @@ class ImageTypeImageFileUpload(Thread):
                                                                                  image_url,
                                                                                  remote_url_list[ii],
                                                                                  model_info_list[ii].get('modelCode'),
-                                                                                 model_info_list[ii].get('detectTargetCode'),
+                                                                                 model_info_list[ii].get(
+                                                                                     'detectTargetCode'),
                                                                                  analyse_results=result))
-                                                                             
-                                                                             
-                                                                             
 
-                                
                                 for msg in msg_list:
                                     put_queue(fb_queue, msg, timeout=2, is_ex=False)
                         else:

concurrency/IntelligentRecognitionProcess.py

@@ -62,8 +62,9 @@ class IntelligentRecognitionProcess(Process):
         # 发送waitting消息
         put_queue(self._fb_queue, message_feedback(self._msg["request_id"], AnalysisStatus.WAITING.value,
                                                    self._analyse_type, progress=init_progess), timeout=2, is_ex=True)
-        self._storage_source = self._context['service']['storage_source']                                           
+        self._storage_source = self._context['service']['storage_source']
         self._algStatus = False
+
     def sendEvent(self, eBody):
         put_queue(self.event_queue, eBody, timeout=2, is_ex=True)
 
@@ -92,8 +93,6 @@ class IntelligentRecognitionProcess(Process):
         return hb_thread
 
 
-
-
 class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
     __slots__ = ()
 
@@ -112,19 +111,16 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
         pullProcess.start()
         return pullProcess
 
-   
     def upload_video(self,base_dir, env, request_id, orFilePath, aiFilePath):
         if self._storage_source==1:
-            minioSdk = MinioSdk(base_dir, env, request_id )
+            minioSdk = MinioSdk(base_dir, env, request_id)
             upload_video_thread_or = Common(minioSdk.put_object, orFilePath, "or_online_%s.mp4" % request_id)
             upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "ai_online_%s.mp4" % request_id)
-        else:    
+        else:
             aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
             upload_video_thread_or = Common(aliyunVodSdk.get_play_url, orFilePath, "or_online_%s" % request_id)
             upload_video_thread_ai = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
-        
-        
-        
+
         upload_video_thread_or.setDaemon(True)
         upload_video_thread_ai.setDaemon(True)
         upload_video_thread_or.start()
@@ -132,6 +128,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
         or_url = upload_video_thread_or.get_result()
         ai_url = upload_video_thread_ai.get_result()
         return or_url, ai_url
+
     '''    
     @staticmethod
     def upload_video(base_dir, env, request_id, orFilePath, aiFilePath):
@@ -145,7 +142,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
         or_url = upload_video_thread_or.get_result()
         ai_url = upload_video_thread_ai.get_result()
         return or_url, ai_url
-    '''    
+    '''
 
     @staticmethod
     def ai_normal_dtection(model, frame, request_id):
@@ -225,7 +222,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
         ex = None
         # 拉流进程、推流进程、心跳线程
         pull_process, push_process, hb_thread = None, None, None
-        
+
         # 事件队列、拉流队列、心跳队列、反馈队列
         event_queue, pull_queue, hb_queue, fb_queue = self.event_queue, self._pull_queue, self._hb_queue, self._fb_queue
 
@@ -240,14 +237,14 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
             # 启动拉流进程(包含拉流线程, 图片上传线程,mqtt读取线程)
             # 拉流进程初始化时间长, 先启动
             pull_process = self.start_pull_stream(msg, context, fb_queue, pull_queue, image_queue, analyse_type, 25)
-            #print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #7.0, 
+            #print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #7.0,
             # 启动心跳线程
             hb_thread = self.start_heartbeat(fb_queue, hb_queue, request_id, analyse_type, context)
 
             # print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #7.0,
             # 加载算法模型
             model_array = get_model(msg, context, analyse_type)
-            #print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #9.5
+            # print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #9.5
             # 启动推流进程
             push_process = self.start_push_stream(msg, push_queue, image_queue, push_ex_queue, hb_queue, context)
             #print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
@@ -300,7 +297,8 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                     for i, model in enumerate(model_array):
                                         model_conf, code = model
                                         if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
-                                           ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
+                                           ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
                                             if draw_config.get(code) is None:
                                                 draw_config[code] = {}
                                                 draw_config["font_config"] = model_conf[4]
@@ -310,7 +308,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                         else:
                                             model_param = model_conf[1]
                                             # (modeType, model_param, allowedList, names, rainbows)
-                                            MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0],
+                                            MODEL_CONFIG[code][2](frame_list[0][0].shape[1], frame_list[0][0].shape[0],
                                                                   model_conf)
                                             if draw_config.get("font_config") is None:
                                                 draw_config["font_config"] = model_param['font_config']
@@ -326,16 +324,16 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                 # print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
                                 # 多线程并发处理, 经过测试两个线程最优
                                 det_array = []
-                                for i, frame in enumerate(frame_list):
+                                for i, [frame,_] in enumerate(frame_list):
                                     det_result = t.submit(self.obj_det, self, model_array, frame, task_status,
                                                           frame_index_list[i], tt, request_id)
                                     det_array.append(det_result)
                                 push_objs = [det.result() for det in det_array]
-                                #print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)  
+                                # print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
                                 put_queue(push_queue,
                                           (1, (frame_list, frame_index_list, all_frames, draw_config, push_objs)),
                                           timeout=2, is_ex=True)
-                                #print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)            
+                                # print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
                                 del det_array, push_objs
                             del frame_list, frame_index_list, all_frames
                         elif pull_result[0] == 1:
@@ -447,12 +445,12 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
 class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
     __slots__ = ()
 
-    def upload_video(self,base_dir, env, request_id, aiFilePath):
+    def upload_video(self, base_dir, env, request_id, aiFilePath):
         aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
         upload_video_thread_ai = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
 
-        if self._storage_source==1:
-            minioSdk = MinioSdk(base_dir, env, request_id )
+        if self._storage_source == 1:
+            minioSdk = MinioSdk(base_dir, env, request_id)
             upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "ai_online_%s.mp4" % request_id)
         else:
             aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
@@ -473,6 +471,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
         ai_url = upload_video_thread_ai.get_result()
         return ai_url
     '''
+
     @staticmethod
     def ai_normal_dtection(model, frame, request_id):
         model_conf, code = model
@@ -626,7 +625,8 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                     for i, model in enumerate(model_array):
                                         model_conf, code = model
                                         if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
-                                           ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
+                                           ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
                                             if draw_config.get(code) is None:
                                                 draw_config[code] = {}
                                                 draw_config["font_config"] = model_conf[4]
@@ -637,7 +637,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                         else:
                                             model_param = model_conf[1]
                                             # (modeType, model_param, allowedList, names, rainbows)
-                                            MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0],
+                                            MODEL_CONFIG[code][2](frame_list[0][0].shape[1], frame_list[0][0].shape[0],
                                                                   model_conf)
                                             if draw_config.get("font_config") is None:
                                                 draw_config["font_config"] = model_param['font_config']
@@ -651,7 +651,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
 
 
                                 det_array = []
-                                for i, frame in enumerate(frame_list):
+                                for i, [frame,_] in enumerate(frame_list):
                                     det_result = t.submit(self.obj_det, self, model_array, frame, task_status,
                                                           frame_index_list[i], tt, request_id)
                                     det_array.append(det_result)
@@ -766,7 +766,7 @@ class PhotosIntelligentRecognitionProcess(Process):
         put_queue(self._fb_queue, message_feedback(self._msg["request_id"], AnalysisStatus.WAITING.value,
                                                    self._analyse_type, progress=init_progess), timeout=2, is_ex=True)
         self.build_logo(self._msg, self._context)
-        self._storage_source = self._context['service']['storage_source']     
+        self._storage_source = self._context['service']['storage_source']
 
     @staticmethod
     def build_logo(msg, context):
@@ -943,7 +943,7 @@ class PhotosIntelligentRecognitionProcess(Process):
             logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id)
             raise e
 
-    #自研究车牌模型
+    # 自研究车牌模型
     def carplate_rec(self, imageUrl, mod, image_queue, request_id):
         try:
             # model_conf： modeType, allowedList, detpar, ocrmodel, rainbows
@@ -982,7 +982,6 @@ class PhotosIntelligentRecognitionProcess(Process):
             # param = [image, new_device, model, par, img_type, request_id]
             # model_conf, frame, device, requestId
             dataBack = MODEL_CONFIG[code][3]([[modeType, device, model, postPar], image, request_id])[0][2]
-            logger.info("当前人数：{}", dataBack[0][0])
             dets[code][0] = dataBack
             if not dataBack:
                 logger.info("当前页面无人")
@@ -1055,6 +1054,8 @@ class PhotosIntelligentRecognitionProcess(Process):
             ai_result_list = p_result[2]
             for ai_result in ai_result_list:
                 box, score, cls = xywh2xyxy2(ai_result)
+                if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
+                    box.append(ai_result[-1])
                 # 如果检测目标在识别任务中,继续处理
                 if cls in allowedList:
                     label_array = label_arraylist[cls]
@@ -1212,7 +1213,8 @@ class PhotosIntelligentRecognitionProcess(Process):
         image_thread.setDaemon(True)
         image_thread.start()
         return image_thread
-    def check_ImageUrl_Vaild(self,url,timeout=1):
+
+    def check_ImageUrl_Vaild(self, url, timeout=1):
         try:
             # 发送 HTTP 请求，尝试访问图片
             response = requests.get(url, timeout=timeout)  # 设置超时时间为 10 秒
@@ -1243,8 +1245,7 @@ class PhotosIntelligentRecognitionProcess(Process):
                                                  ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],
                                                  ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1]), timeout=2) 
 
-                return 
-
+                return
 
         with ThreadPoolExecutor(max_workers=1) as t:
             try:
@@ -1271,7 +1272,8 @@ class PhotosIntelligentRecognitionProcess(Process):
                         result = t.submit(self.carpalteRec, imageUrls, model, image_queue, request_id)
                         task_list.append(result)
                     # 人群计数模型
-                    elif model[1] == ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1]:
+                    elif model[1] == ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] or \
+                            model[1] == ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1]:
                         result = t.submit(self.denscrowdcountRec, imageUrls, model, image_queue, request_id)
                         task_list.append(result)
                     else:
@@ -1321,6 +1323,7 @@ class ScreenRecordingProcess(Process):
                   recording_feedback(self._msg["request_id"], RecordingStatus.RECORDING_WAITING.value[0]),
                   timeout=1, is_ex=True)
         self._storage_source = self._context['service']['storage_source']
+
     def sendEvent(self, result):
         put_queue(self._event_queue, result, timeout=2, is_ex=True)
 
@@ -1486,9 +1489,9 @@ class ScreenRecordingProcess(Process):
         clear_queue(self._hb_queue)
         clear_queue(self._pull_queue)
 
-    def upload_video(self,base_dir, env, request_id, orFilePath):
-        if self._storage_source==1:
-            minioSdk = MinioSdk(base_dir, env, request_id )
+    def upload_video(self, base_dir, env, request_id, orFilePath):
+        if self._storage_source == 1:
+            minioSdk = MinioSdk(base_dir, env, request_id)
             upload_video_thread_ai = Common(minioSdk.put_object, aiFilePath, "%s/ai_online.mp4" % request_id)
         else:
             aliyunVodSdk = ThAliyunVodSdk(base_dir, env, request_id)
@@ -1498,6 +1501,7 @@ class ScreenRecordingProcess(Process):
         upload_video_thread_ai.start()
         or_url = upload_video_thread_ai.get_result()
         return or_url
+
     '''    
     @staticmethod
     def upload_video(base_dir, env, request_id, orFilePath):

concurrency/PullMqttThread.py

@@ -1,142 +1,163 @@
-# -*- coding: utf-8 -*-
-from threading import Thread
-from time import sleep, time
-from traceback import format_exc
-
-from loguru import logger
-from common.YmlConstant import mqtt_yml_path
-from util.RWUtils import getConfigs
-from common.Constant import init_progess
-from enums.AnalysisStatusEnum import AnalysisStatus
-from entity.FeedBack import message_feedback
-from enums.ExceptionEnum import ExceptionType
-from exception.CustomerException import ServiceException
-from util.QueUtil import get_no_block_queue, put_queue, clear_queue
-from multiprocessing import Process, Queue
-import paho.mqtt.client as mqtt  
-import json,os
-class PullMqtt(Thread):
-    __slots__ = ('__fb_queue', '__mqtt_list', '__request_id', '__analyse_type', "_context")
-
-    def __init__(self, *args):
-        super().__init__()
-        self.__fb_queue, self.__mqtt_list, self.__request_id, self.__analyse_type, self._context = args
-        
-        base_dir, env = self._context["base_dir"], self._context["env"]
-        self.__config = getConfigs(os.path.join(base_dir, mqtt_yml_path % env))
-        
-        self.__broker = self.__config["broker"]
-        self.__port = self.__config["port"]
-        self.__topic = self.__config["topic"]
-        self.__lengthMqttList = self.__config["length"]
-        
-    
-    def put_queue(self,__queue,data):
-        if __queue.full():
-            a = __queue.get()
-        __queue.put( data,block=True, timeout=2 )    
-    def on_connect(self,client,userdata,flags,rc):
-        client.subscribe(self.__topic)
-
-
-
-    # 当接收到MQTT消息时，回调函数
-    def on_message(self,client, userdata, msg):
-        # 将消息解码为JSON格式
-        payload = msg.payload.decode('utf-8')
-        data = json.loads(payload)
-        #logger.info(str(data))
-        
-
-        # 解析位姿信息
-        lon = data.get("lon")
-        lat = data.get("lat")
-        alt = data.get("alt")
-        yaw = data.get("yaw")
-        pitch = data.get("pitch")
-        roll = data.get("roll")
-        
-        if len(self.__mqtt_list) == self.__lengthMqttList:
-            self.__mqtt_list.pop(0)
-        self.__mqtt_list.append(data)   
-    
-       
-        # 打印无人机的位姿信息
-        #print(f"Longitude: {lon}, Latitude: {lat}, Altitude: {alt}, sat:{data.get('satcount')} , list length:{len(self.__mqtt_list)}")
-       
-    def mqtt_connect(self):
-        # 创建客户端  
-        self.client = mqtt.Client() 
-        self.client.on_connect = self.on_connect 
-        # 设置回调函数  
-        self.client.on_message = self.on_message  
-
-        # 连接到 Broker  
-        self.client.connect(self.__broker, self.__port)  
-
-        # 订阅主题  
-        self.client.subscribe(self.__topic) 
-        # 循环等待并处理网络事件  
-        self.client.loop_forever()
-        
-    def mqtt_disconnect(self):
-        start_time = time()
-        while True:
-            if time() - start_time > service_timeout:
-                logger.error("MQTT读取超时, requestId: %s,限定时间：%.1s , 已运行：%.1fs"%(request_id,service_timeout, time() - start_time))
-                raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
-                                       ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
-                client.loop_stop()  # 停止循环
-                client.disconnect()  # 断开连接
-
-    def run(self):
-        request_id, mqtt_list, progress = self.__request_id, self.__mqtt_list, init_progess
-        analyse_type, fb_queue = self.__analyse_type, self.__fb_queue
-        #service_timeout = int(self.__config["service"]["timeout"]) + 120
-        
-        try:
-            logger.info("开始MQTT读取线程！requestId:{}", request_id)
-            mqtt_init_num = 0
-            self.mqtt_connect()
-  
-        except Exception:
-            logger.error("MQTT线程异常:{}, requestId:{}", format_exc(), request_id)
-        finally:
-            mqtt_list = []
-            logger.info("MQTT线程停止完成！requestId:{}", request_id)
- 
- 
-def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context):
-    mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
-    mqtt_thread.setDaemon(True)
-    mqtt_thread.start()
-    return mqtt_thread 
-def start_PullVideo(mqtt_list):
-    for i in range(1000):
-        sleep(1)
-        if len(mqtt_list)>=10:
-            print( mqtt_list[4])
-        print(i,len(mqtt_list))    
-if __name__=="__main__": 
-    #context = {'service':{'timeout':3600},'mqtt':{
-    #    'broker':"101.133.163.127",'port':1883,'topic':"test/topic","length":10}
-    #    }
-    context = {
-            'base_dir':'/home/th/WJ/test/tuoheng_algN',
-            'env':'test'
-    
-    }    
-    analyse_type = '1'
-    request_id = '123456789'
-    event_queue,  pull_queue, mqtt_list,  image_queue,  push_queue,  push_ex_queue = Queue(), Queue(10), [], Queue(), Queue(), Queue()
-    fb_queue =   Queue()          
-    mqtt_thread = start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
-
-
-    start_PullVideo(mqtt_list)
-    print('---line117--')
-    
-    
-
-    #mqtt_thread.join()
+# -*- coding: utf-8 -*-
+from threading import Thread
+from time import sleep, time
+from traceback import format_exc
+
+from loguru import logger
+from common.YmlConstant import mqtt_yml_path
+from util.RWUtils import getConfigs
+from common.Constant import init_progess
+from enums.AnalysisStatusEnum import AnalysisStatus
+from entity.FeedBack import message_feedback
+from enums.ExceptionEnum import ExceptionType
+from exception.CustomerException import ServiceException
+from util.QueUtil import get_no_block_queue, put_queue, clear_queue
+from multiprocessing import Process, Queue
+import paho.mqtt.client as mqtt  
+import json,os
+class PullMqtt(Thread):
+    __slots__ = ('__fb_queue', '__mqtt_list', '__request_id', '__analyse_type', "_context" ,'__business')
+
+    def __init__(self, *args):
+        super().__init__()
+        self.__fb_queue, self.__mqtt_list, self.__request_id, self.__analyse_type, self._context, self.__business = args
+        
+        base_dir, env = self._context["base_dir"], self._context["env"]
+        self.__config = getConfigs(os.path.join(base_dir, mqtt_yml_path % env))
+        if self.__business == 0:
+            self.__broker = self.__config['location']["broker"]
+            self.__port = self.__config['location']["port"]
+            self.__topic = self.__config['location']["topic"]
+        elif self.__business == 1:
+            self.__broker = self.__config['invade']["broker"]
+            self.__port = self.__config['invade']["port"]
+            self.__topic = self.__config['invade']["topic"]
+        self.__lengthMqttList = self.__config["length"]
+        
+    
+    def put_queue(self,__queue,data):
+        if __queue.full():
+            a = __queue.get()
+        __queue.put( data,block=True, timeout=2 )    
+    def on_connect(self,client,userdata,flags,rc):
+        client.subscribe(self.__topic)
+
+
+
+    # 当接收到MQTT消息时，回调函数
+    def on_location(self,client, userdata, msg):
+        # 将消息解码为JSON格式
+        payload = msg.payload.decode('utf-8')
+        data = json.loads(payload)
+        #logger.info(str(data))
+        # 解析位姿信息
+        lon = data.get("lon")
+        lat = data.get("lat")
+        alt = data.get("alt")
+        yaw = data.get("yaw")
+        pitch = data.get("pitch")
+        roll = data.get("roll")
+        
+        if len(self.__mqtt_list) == self.__lengthMqttList:
+            self.__mqtt_list.pop(0)
+        self.__mqtt_list.append([self.__business,data])
+    
+       
+        # 打印无人机的位姿信息
+        #print(f"Longitude: {lon}, Latitude: {lat}, Altitude: {alt}, sat:{data.get('satcount')} , list length:{len(self.__mqtt_list)}")
+
+    def on_invade(self, client, userdata, msg):
+        # 将消息解码为JSON格式
+        payload = msg.payload.decode('utf-8')
+        data = json.loads(payload)
+        # logger.info(str(data))
+        # 解析位姿信息
+        points = data.get("points")
+
+        if len(self.__mqtt_list) == self.__lengthMqttList:
+            self.__mqtt_list.pop(0)
+        self.__mqtt_list.append([self.__business,points])
+
+        # 打印无人机的位姿信息
+        # print(f"Longitude: {lon}, Latitude: {lat}, Altitude: {alt}, sat:{data.get('satcount')} , list length:{len(self.__mqtt_list)}")
+       
+    def mqtt_connect(self):
+        # 创建客户端  
+        self.client = mqtt.Client()
+        self.client.on_connect = self.on_connect
+        if self.__business == 0:
+            # 设置回调函数
+            self.client.on_message = self.on_location
+        elif self.__business == 1:
+            # 设置回调函数
+            self.client.on_message = self.on_invade
+
+        # 连接到 Broker  
+        self.client.connect(self.__broker, self.__port)  
+
+        # 订阅主题  
+        self.client.subscribe(self.__topic) 
+        # 循环等待并处理网络事件  
+        self.client.loop_forever()
+        
+    def mqtt_disconnect(self):
+        start_time = time()
+        while True:
+            if time() - start_time > service_timeout:
+                logger.error("MQTT读取超时, requestId: %s,限定时间：%.1s , 已运行：%.1fs"%(request_id,service_timeout, time() - start_time))
+                raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
+                                       ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
+                client.loop_stop()  # 停止循环
+                client.disconnect()  # 断开连接
+
+    def run(self):
+        request_id, mqtt_list, progress = self.__request_id, self.__mqtt_list, init_progess
+        analyse_type, fb_queue = self.__analyse_type, self.__fb_queue
+        #service_timeout = int(self.__config["service"]["timeout"]) + 120
+        
+        try:
+            logger.info("开始MQTT读取线程！requestId:{}", request_id)
+            mqtt_init_num = 0
+            self.mqtt_connect()
+  
+        except Exception:
+            logger.error("MQTT线程异常:{}, requestId:{}", format_exc(), request_id)
+        finally:
+            mqtt_list = []
+            logger.info("MQTT线程停止完成！requestId:{}", request_id)
+ 
+ 
+def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context):
+    mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
+    mqtt_thread.setDaemon(True)
+    mqtt_thread.start()
+    return mqtt_thread 
+def start_PullVideo(mqtt_list):
+    for i in range(1000):
+        sleep(1)
+        if len(mqtt_list)>=10:
+            print( mqtt_list[4])
+        print(i,len(mqtt_list))    
+if __name__=="__main__": 
+    #context = {'service':{'timeout':3600},'mqtt':{
+    #    'broker':"101.133.163.127",'port':1883,'topic':"test/topic","length":10}
+    #    }
+    context = {
+            'base_dir':'/home/th/WJ/test/tuoheng_algN',
+            'env':'test'
+    
+    }    
+    analyse_type = '1'
+    request_id = '123456789'
+    event_queue,  pull_queue, mqtt_list,  image_queue,  push_queue,  push_ex_queue = Queue(), Queue(10), [], Queue(), Queue(), Queue()
+    fb_queue =   Queue()          
+    mqtt_thread = start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
+
+
+    start_PullVideo(mqtt_list)
+    print('---line117--')
+    
+    
+
+    #mqtt_thread.join()
         

concurrency/PullVideoStreamProcess.py

@@ -35,15 +35,15 @@ class PullVideoStreamProcess(Process):
         put_queue(self._command_queue, result, timeout=2, is_ex=True)
 
     @staticmethod
-    def start_File_upload(fb_queue, context, msg, image_queue, analyse_type,mqtt_list):
-        image_thread = ImageFileUpload(fb_queue, context, msg, image_queue, analyse_type,mqtt_list)
+    def start_File_upload(fb_queue, context, msg, image_queue, analyse_type):
+        image_thread = ImageFileUpload(fb_queue, context, msg, image_queue, analyse_type)
         image_thread.setDaemon(True)
         image_thread.start()
         return image_thread
         
     @staticmethod
-    def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context):
-        mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
+    def start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context,business):
+        mqtt_thread = PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context,business)
         mqtt_thread.setDaemon(True)
         mqtt_thread.start()
         return mqtt_thread
@@ -81,13 +81,14 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
             # 初始化日志
             init_log(base_dir, env)
             logger.info("开启启动实时视频拉流进程, requestId:{},pid:{},ppid:{}", request_id,os.getpid(),os.getppid())
-            
-            #开启mqtt
-            if service["mqtt_flag"]==1:
-                mqtt_thread = self.start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context)
+
+            # 开启mqtt
+            if service['mqtt']["flag"] == 1:
+                business = service['mqtt']["business"]
+                mqtt_thread = self.start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context, business)
 
             # 开启图片上传线程
-            image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type,mqtt_list)
+            image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)
             cv2_init_num, init_pull_num, concurrent_frame = 0, 1, 1
             start_time, pull_stream_start_time, read_start_time, full_timeout = time(), None, None, None
             while True:
@@ -129,7 +130,7 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
                 frame, pull_p, width, height = pull_read_video_stream(pull_p, pull_url, width, height, width_height_3,
                                                                       w_2, h_2, request_id)
                 if pull_queue.full():
-                    logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
+                    #logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
                     if full_timeout is None:
                         full_timeout = time()
                     if time() - full_timeout > 180:
@@ -171,7 +172,7 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
                     sleep(1)
                     continue
                 init_pull_num, read_start_time = 1, None
-                frame_list.append(frame)
+                frame_list.append([frame, mqtt_list])
                 frame_index_list.append(concurrent_frame)
                 if len(frame_list) >= frame_num:
                     put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1, is_ex=True)
@@ -222,10 +223,11 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
 
     def run(self):
         msg, context, frame_num, analyse_type = self._msg, self._context, self._frame_num, self._analyse_type
-        request_id, base_dir, env, pull_url = msg["request_id"], context['base_dir'], context['env'], msg["pull_url"]
+        request_id, base_dir, env, pull_url, service = msg["request_id"], context['base_dir'], context['env'], msg["pull_url"], context["service"]
         ex, service_timeout, full_timeout = None, int(context["service"]["timeout"]) + 120, None
-        command_queue, pull_queue, image_queue, fb_queue = self._command_queue, self._pull_queue, self._image_queue, \
-            self._fb_queue
+
+        command_queue, pull_queue, image_queue, fb_queue, mqtt_list = self._command_queue, self._pull_queue, self._image_queue, \
+            self._fb_queue, self._mqtt_list
         image_thread, pull_p = None, None
         width, height, width_height_3, all_frames, w_2, h_2 = None, None, None, 0, None, None
         frame_list, frame_index_list = [], []
@@ -235,8 +237,12 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
             init_log(base_dir, env)
             logger.info("开启离线视频拉流进程, requestId:{}", request_id)
 
+            #开启mqtt
+            if service['mqtt']["flag"]==1:
+                business = service['mqtt']["business"]
+                mqtt_thread = self.start_PullMqtt(fb_queue, mqtt_list, request_id, analyse_type, context, business)
             # 开启图片上传线程
-            image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type,[])
+            image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)
 
             # 初始化拉流工具类
             cv2_init_num, concurrent_frame = 0, 1
@@ -269,7 +275,7 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
                     width, height, width_height_3, all_frames, w_2, h_2 = build_video_info(pull_url, request_id)
                     continue
                 if pull_queue.full():
-                    logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
+                    #logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
                     if full_timeout is None:
                         full_timeout = time()
                     if time() - full_timeout > 180:
@@ -306,7 +312,7 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
                                                ExceptionType.READSTREAM_TIMEOUT_EXCEPTION.value[1])
                     logger.info("离线拉流线程结束, requestId: {}", request_id)
                     break
-                frame_list.append(frame)
+                frame_list.append([frame,mqtt_list])
                 frame_index_list.append(concurrent_frame)
                 if len(frame_list) >= frame_num:
                     put_queue(pull_queue, (4, (frame_list, frame_index_list, all_frames)), timeout=1, is_ex=True)

concurrency/PushVideoStreamProcess.py

@@ -23,7 +23,7 @@ from util.Cv2Utils import video_conjuncing, write_or_video, write_ai_video, push
 from util.ImageUtils import url2Array, add_water_pic
 from util.LogUtils import init_log
 
-from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, draw_name_joint
+from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, xy2xyxy, draw_name_joint, plot_one_box_auto, draw_name_ocr,draw_name_crowd,draw_transparent_red_polygon
 
 from util.QueUtil import get_no_block_queue, put_queue, clear_queue
 
@@ -36,11 +36,10 @@ class PushStreamProcess(Process):
         # 传参
         self._msg, self._push_queue, self._image_queue, self._push_ex_queue, self._hb_queue, self._context = args
         self._algStatus = False # 默认关闭
-        self._algSwitch = self._context['service']['algSwitch'] 
-        
-        
-        #0521:
-        default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"    
+        self._algSwitch = self._context['service']['algSwitch']
+
+        # 0521:
+        default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
         if default_enabled:
             print("执行默认程序（defaultEnabled=True）")
             self._algSwitch = True
@@ -49,16 +48,9 @@ class PushStreamProcess(Process):
             print("执行替代程序（defaultEnabled=False）")
             # 这里放非默认逻辑的代码
             self._algSwitch = False
-        
-            
+
         print("---line53 :PushVideoStreamProcess.py---",self._algSwitch)
-            
-           
-        
-        
-        
-                  
-       
+
     def build_logo_url(self):
         logo = None
         if self._context["video"]["video_add_water"]:
@@ -138,15 +130,26 @@ class OnPushStreamProcess(PushStreamProcess):
                     if push_r is not None:
                         if push_r[0] == 1:
                             frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
-                            for i, frame in enumerate(frame_list):
+                            # 处理每1帧
+                            for i, [frame,mqtt_list] in enumerate(frame_list):
+                                # mqtt传参
+                                border = None
+                                mqttPares = None
+                                if len(mqtt_list) >= 1:
+                                    mqttPares = mqtt_list[0]
+                                    if mqttPares[0] == 1:
+                                        border = mqttPares[1]
                                 pix_dis = int((frame.shape[0]//10)*1.2)
                                 # 复制帧用来画图
                                 copy_frame = frame.copy()
+                                if border is not None:
+                                    copy_frame = draw_transparent_red_polygon(copy_frame, np.array(border, np.int32),alpha=0.25)
                                 det_xywh, thread_p = {}, []
-                                det_xywh2 = {}
+                                det_xywh2 = {'mqttPares':mqttPares}
                                 # 所有问题的矩阵集合
                                 qs_np = None
                                 qs_reurn = []
+                                bp_np = None
                                 for det in push_objs[i]:
                                     code, det_result = det
                                     # 每个单独模型处理
@@ -155,17 +158,42 @@ class OnPushStreamProcess(PushStreamProcess):
                                         font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
                                         rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
                                         for qs in det_result:
-                                            try:   # 应对NaN情况
-                                                box, score, cls = xywh2xyxy2(qs)
-                                            except:
-                                                continue
-                                            if cls not in allowedList or score < frame_score:
-                                                continue
-                                            label_array, color = label_arrays[cls], rainbows[cls]
-                                            if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
-                                                rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
+                                            # 自研车牌模型处理
+                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
+                                                cls = 0
+                                                box = xy2xyxy(qs[1])
+                                                score = None
+                                                color = rainbows[cls]
+                                                label_array = None
+                                                rr = t.submit(draw_name_ocr, qs, copy_frame, color)
+                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                                cls = 0
+                                                # crowdlabel, points = qs
+                                                box = [(0, 0), (0, 0), (0, 0), (0, 0)]
+                                                score = None
+                                                color = rainbows[cls]
+                                                label_array = None
+                                                rr = t.submit(draw_name_crowd, qs, copy_frame, color)
                                             else:
-                                                rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
+                                                try:  # 应对NaN情况
+                                                    box, score, cls = xywh2xyxy2(qs)
+                                                    if cls not in allowedList or score < frame_score:
+                                                        continue
+                                                    if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
+                                                        # 借score作为points点集
+                                                        box.append(qs[-1])
+                                                except:
+                                                    continue
+                                                label_array, color = label_arrays[cls], rainbows[cls]
+                                                if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
+                                                    rr = t.submit(draw_name_joint, box, copy_frame,
+                                                                  draw_config[code]["label_dict"], score, color,
+                                                                  font_config, qs[6])
+                                                else:
+                                                    rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config, border=border)
+
+
                                             thread_p.append(rr)
                                             if det_xywh.get(code) is None:
                                                 det_xywh[code] = {}
@@ -174,32 +202,35 @@ class OnPushStreamProcess(PushStreamProcess):
                                                 if cd is None:
                                                     det_xywh[code][cls] = [[cls, box, score, label_array, color]]
                                                 else:
-                                                    det_xywh[code][cls].append([cls, box, score, label_array, color])    
+                                                    det_xywh[code][cls].append([cls, box, score, label_array, color])
                                                 if qs_np is None:
-                                                    qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1], 
-                                                                    box[2][0], box[2][1], box[3][0], box[3][1], 
+                                                    qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
+                                                                    box[2][0], box[2][1], box[3][0], box[3][1],
                                                                     score, cls, code],dtype=np.float32)
                                                 else:
-                                                    result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1], 
+                                                    result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
                                                                         box[2][0], box[2][1], box[3][0], box[3][1],
                                                                         score, cls, code],dtype=np.float32)
                                                     qs_np = np.row_stack((qs_np, result_li))
-                                                    
+
+                                            if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
+                                                if bp_np is None:
+                                                    bp_np = np.array([box[0][0], box[0][1], box[-1]], dtype=object)
+                                                else:
+                                                    bp_li = np.array([box[0][0], box[0][1], box[-1]], dtype=object)
+                                                    bp_np = np.row_stack((bp_np, bp_li))
+
                                 if logo:
                                     frame = add_water_pic(frame, logo, request_id)
                                     copy_frame = add_water_pic(copy_frame, logo, request_id)
                                 if len(thread_p) > 0:
                                     for r in thread_p:
                                         r.result()
-                                #print('----line173:',self._algSwitch,self._algStatus)        
+                                #print('----line173:',self._algSwitch,self._algStatus)
                                 if self._algSwitch and (not self._algStatus):
-                                    # frame_merge = video_conjuncing(frame, frame.copy())
-                                    
-                                    frame_merge = frame.copy()
-                                else:    
-                                    # frame_merge = video_conjuncing(frame, copy_frame)
-                                    
-                                    frame_merge = copy_frame
+                                    frame_merge = video_conjuncing(frame, frame.copy())
+                                else:
+                                    frame_merge = video_conjuncing(frame, copy_frame)
                                 # 写原视频到本地
                                 write_or_video_result = t.submit(write_or_video, frame, orFilePath, or_video_file,
                                                                  or_write_status, request_id)
@@ -211,7 +242,7 @@ class OnPushStreamProcess(PushStreamProcess):
                                 # 如果有问题, 走下面的逻辑
                                 if qs_np is not None:
                                     if len(qs_np.shape) == 1:
-                                            qs_np = qs_np[np.newaxis,...]
+                                        qs_np = qs_np[np.newaxis,...]
                                     qs_np_id = qs_np.copy()
                                     b = np.ones(qs_np_id.shape[0])
                                     qs_np_id = np.column_stack((qs_np_id,b))
@@ -219,7 +250,7 @@ class OnPushStreamProcess(PushStreamProcess):
                                         if picture_similarity:
                                             qs_np_tmp = qs_np_id.copy()
                                         b = np.zeros(qs_np.shape[0])
-                                        qs_reurn = np.column_stack((qs_np,b))  
+                                        qs_reurn = np.column_stack((qs_np,b))
                                     else:
                                         qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
                                         if picture_similarity:
@@ -237,7 +268,7 @@ class OnPushStreamProcess(PushStreamProcess):
                                         if q[11] >= 1:
                                             cls = int(q[9])
                                             if not (cls in new_lab):
-                                                continue        # 为了防止其他类别被带出
+                                                continue  # 为了防止其他类别被带出
                                             code = str(int(q[10])).zfill(3)
                                             if det_xywh2.get(code) is None:
                                                 det_xywh2[code] = {}
@@ -245,17 +276,29 @@ class OnPushStreamProcess(PushStreamProcess):
                                             score = q[8]
                                             rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
                                             label_array, color = label_arrays[cls], rainbows[cls]
-                                            box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])), 
-                                                    (int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
+                                            box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
+                                                   (int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
+                                            if bp_np is not None:
+                                                if len(bp_np.shape)==1:
+                                                    bp_np = bp_np[np.newaxis, ...]
+                                                for bp in bp_np:
+                                                    if np.array_equal(bp[:2], np.array([int(q[0]), int(q[1])])):
+                                                        box.append(bp[-1])
                                             is_new = False
                                             if q[11] == 1:
                                                 is_new = True
+                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
+                                                ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                                box = qs
                                             if cd is None:
-                                                det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]] 
+                                                det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
                                             else:
-                                                det_xywh2[code][cls].append([cls, box, score, label_array, color, is_new])
+                                                det_xywh2[code][cls].append(
+                                                    [cls, box, score, label_array, color, is_new])
                                     if len(det_xywh2) > 0:
-                                        put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
+                                        put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames,
+                                                                    draw_config["font_config"]]))
 
                                 push_p = push_stream_result.result(timeout=60)
                                 ai_video_file = write_ai_video_result.result(timeout=60)
@@ -264,7 +307,7 @@ class OnPushStreamProcess(PushStreamProcess):
                         if push_r[0] == 2:
                             logger.info("拉流进程收到控制命令为:{}, requestId: {}",push_r[1] ,request_id)
                             if 'algStart' == push_r[1]: self._algStatus = True;logger.info("算法识别开启, requestId: {}", request_id)
-                            if 'algStop' == push_r[1]:  self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)    
+                            if 'algStop' == push_r[1]:  self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
                             if 'stop' == push_r[1]:
                                 logger.info("停止推流进程, requestId: {}", request_id)
                                 break
@@ -272,7 +315,7 @@ class OnPushStreamProcess(PushStreamProcess):
                                 ex_status = False
                                 logger.info("停止推流进程, requestId: {}", request_id)
                                 break
-                                
+
                         del push_r
                     else:
                         sleep(1)
@@ -351,40 +394,72 @@ class OffPushStreamProcess(PushStreamProcess):
                         # [(2, 操作指令)] 指令操作
                         if push_r[0] == 1:
                             frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
+
                             # 处理每一帧图片
-                            for i, frame in enumerate(frame_list):
+                            for i, [frame,mqtt_list] in enumerate(frame_list):
+                                # mqtt传参
+                                border = None
+                                mqttPares = None
+                                if len(mqtt_list) >= 1:
+                                    mqttPares = mqtt_list[0]
+                                    if mqttPares[0] == 1:
+                                        border = mqttPares[1]
                                 pix_dis = int((frame.shape[0]//10)*1.2)
                                 if frame_index_list[i] % 300 == 0 and frame_index_list[i] <= all_frames:
                                     task_process = "%.2f" % (float(frame_index_list[i]) / float(all_frames))
                                     put_queue(hb_queue, {"hb_value": task_process}, timeout=2)
                                 # 复制帧用来画图
                                 copy_frame = frame.copy()
+                                if border is not None:
+                                    copy_frame = draw_transparent_red_polygon(copy_frame, np.array(border, np.int32),alpha=0.25)
                                 # 所有问题记录字典
                                 det_xywh, thread_p = {}, []
-                                det_xywh2 = {}
+                                det_xywh2 = {'mqttPares':mqttPares}
                                 # 所有问题的矩阵集合
                                 qs_np = None
                                 qs_reurn = []
+                                bp_np = None
                                 for det in push_objs[i]:
                                     code, det_result = det
-                               
                                     # 每个单独模型处理
                                     # 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
                                     if len(det_result) > 0:
                                         font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
                                         rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
                                         for qs in det_result:
-                                            box, score, cls = xywh2xyxy2(qs)
-                                      
-                                            if cls not in allowedList or score < frame_score:
-                                                continue
-                                            label_array, color = label_arrays[cls], rainbows[cls]
-                                            if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
-                                                rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
+                                            # 自研车牌模型处理
+                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
+                                                cls = 0
+                                                box = xy2xyxy(qs[1])
+                                                score = None
+                                                color = rainbows[cls]
+                                                label_array = None
+                                                label_arrays = [None]
+                                                rr = t.submit(draw_name_ocr, qs, copy_frame, color)
+
+                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                                cls = 0
+                                                box = [(0,0),(0,0),(0,0),(0,0)]
+                                                score = None
+                                                color = rainbows[cls]
+                                                label_array = None
+                                                rr = t.submit(draw_name_crowd, qs, copy_frame, color)
+
                                             else:
-                                                rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
+                                                box, score, cls = xywh2xyxy2(qs)
+                                                if cls not in allowedList or score < frame_score:
+                                                    continue
+                                                if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
+                                                    box.append(qs[-1])
+
+                                                label_array, color = label_arrays[cls], rainbows[cls]
+                                                if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
+                                                    rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
+                                                else:
+                                                    rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config, border=border)
+
                                             thread_p.append(rr)
-                                 
                                             if det_xywh.get(code) is None:
                                                 det_xywh[code] = {}
                                             cd = det_xywh[code].get(cls)
@@ -392,17 +467,24 @@ class OffPushStreamProcess(PushStreamProcess):
                                                 if cd is None:
                                                     det_xywh[code][cls] = [[cls, box, score, label_array, color]]
                                                 else:
-                                                    det_xywh[code][cls].append([cls, box, score, label_array, color])    
+                                                    det_xywh[code][cls].append([cls, box, score, label_array, color])
                                                 if qs_np is None:
-                                                    qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1], 
-                                                                    box[2][0], box[2][1], box[3][0], box[3][1], 
+                                                    qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
+                                                                    box[2][0], box[2][1], box[3][0], box[3][1],
                                                                     score, cls, code],dtype=np.float32)
                                                 else:
-                                                    result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1], 
+                                                    result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
                                                                         box[2][0], box[2][1], box[3][0], box[3][1],
                                                                         score, cls, code],dtype=np.float32)
                                                     qs_np = np.row_stack((qs_np, result_li))
-                                                           
+
+                                                if ModelType.CITY_FIREAREA_MODEL.value[1]== str(code):
+                                                    if bp_np is None:
+                                                        bp_np = np.array([box[0][0], box[0][1],box[-1]],dtype=object)
+                                                    else:
+                                                        bp_li = np.array([box[0][0], box[0][1],box[-1]],dtype=object)
+                                                        bp_np = np.row_stack((bp_np, bp_li))
+
                                 if logo:
                                     frame = add_water_pic(frame, logo, request_id)
                                     copy_frame = add_water_pic(copy_frame, logo, request_id)
@@ -410,11 +492,9 @@ class OffPushStreamProcess(PushStreamProcess):
                                     for r in thread_p:
                                         r.result()
                                 if self._algSwitch and (not self._algStatus):
-                                    # frame_merge = video_conjuncing(frame, frame.copy())
-                                    frame_merge = frame.copy()
-                                else:    
-                                    # frame_merge = video_conjuncing(frame, copy_frame)
-                                    frame_merge = copy_frame
+                                    frame_merge = video_conjuncing(frame, frame.copy())
+                                else:
+                                    frame_merge = video_conjuncing(frame, copy_frame)
                                 # 写识别视频到本地
                                 write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
                                                                  ai_video_file,
@@ -422,10 +502,9 @@ class OffPushStreamProcess(PushStreamProcess):
                                 push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
                                                               p_push_status, request_id)
 
-                  
                                 if qs_np is not None:
                                     if len(qs_np.shape) == 1:
-                                            qs_np = qs_np[np.newaxis,...]
+                                        qs_np = qs_np[np.newaxis,...]
                                     qs_np_id = qs_np.copy()
                                     b = np.ones(qs_np_id.shape[0])
                                     qs_np_id = np.column_stack((qs_np_id,b))
@@ -433,7 +512,7 @@ class OffPushStreamProcess(PushStreamProcess):
                                         if picture_similarity:
                                             qs_np_tmp = qs_np_id.copy()
                                         b = np.zeros(qs_np.shape[0])
-                                        qs_reurn = np.column_stack((qs_np,b))  
+                                        qs_reurn = np.column_stack((qs_np,b))
                                     else:
                                         qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
                                         if picture_similarity:
@@ -452,7 +531,7 @@ class OffPushStreamProcess(PushStreamProcess):
                                         if q[11] >= 1:
                                             cls = int(q[9])
                                             if not (cls in new_lab):
-                                                continue        # 为了防止其他类别被带出
+                                                continue  # 为了防止其他类别被带出
                                             code = str(int(q[10])).zfill(3)
                                             if det_xywh2.get(code) is None:
                                                 det_xywh2[code] = {}
@@ -460,16 +539,28 @@ class OffPushStreamProcess(PushStreamProcess):
                                             score = q[8]
                                             rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
                                             label_array, color = label_arrays[cls], rainbows[cls]
-                                            box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])), 
-                                                    (int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
+                                            box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
+                                                   (int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
+                                            if bp_np is not None:
+                                                if len(bp_np.shape)==1:
+                                                    bp_np = bp_np[np.newaxis, ...]
+                                                for bp in bp_np:
+                                                    if np.array_equal(bp[:2], np.array([int(q[0]), int(q[1])])):
+                                                        box.append(bp[-1])
                                             is_new = False
                                             if q[11] == 1:
                                                 is_new = True
+
+                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
+                                                ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                                box = qs
                                             if cd is None:
-                                                det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]] 
+                                                det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
                                             else:
-                                                det_xywh2[code][cls].append([cls, box, score, label_array, color, is_new])
-                                    if len(det_xywh2) > 0:
+                                                det_xywh2[code][cls].append(
+                                                    [cls, box, score, label_array, color, is_new])
+                                    if len(det_xywh2) > 1:
                                         put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
                                 push_p = push_stream_result.result(timeout=60)
                                 ai_video_file = write_ai_video_result.result(timeout=60)
@@ -477,7 +568,7 @@ class OffPushStreamProcess(PushStreamProcess):
                         if push_r[0] == 2:
                             logger.info("拉流进程收到控制命令为:{}, requestId: {}",push_r[1] ,request_id)
                             if 'algStart' == push_r[1]: self._algStatus = True;logger.info("算法识别开启, requestId: {}", request_id)
-                            if 'algStop' == push_r[1]:  self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)    
+                            if 'algStop' == push_r[1]:  self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
                             if 'stop' == push_r[1]:
                                 logger.info("停止推流进程, requestId: {}", request_id)
                                 break

config/mqtt/dsp_test_mqtt.yml

@@ -1,10 +1,21 @@
 mqtt_flag: true
-broker : "58.213.148.44"
-port : 1883
-username: "admin"
-password: "admin##123"
-#topic: "/topic/v1/airportFly/%s/aiDroneData"
-topic: "/topic/v1/airportDrone/THJSQ03B2309TPCTD5QV/realTime/data"
-# 存储多少条消息到list里
-length: 10
+# 业务0为经纬度定位，业务1为入侵算法开关
+business: 1
+# 经纬度定位
+location:
+  broker : "58.213.148.44"
+  port : 1883
+  username: "admin"
+  password: "admin##123"
+  #topic: "/topic/v1/airportFly/%s/aiDroneData"
+  topic: "/topic/v1/airportDrone/THJSQ03B2309TPCTD5QV/realTime/data"
 
+# 入侵
+invade:
+  broker : "192.168.11.8"
+  port : 2883
+  #topic: "/topic/v1/airportFly/%s/aiDroneData"
+  topic: "test000/topic"
+# 存储多少条消息到list里
+
+length: 30

config/service/dsp_test_service.yml

@@ -25,10 +25,6 @@ service:
     # 2 模型追踪
     model_type: 1
     limit: 3
-    # 新增：大模型识别配置
-    llm:
-      server_ip: "192.168.10.11"
-      api_url: "http://192.168.10.11:8000/generate"
   task:
     # 任务限制5个
     limit: 5
@@ -37,6 +33,8 @@ service:
   #storage source,0--aliyun,1--minio    
   storage_source: 0
   #是否启用mqtt，0--不用，1--启用
-  mqtt_flag: 0
+  mqtt:
+    flag: 0
+    business: 1
   #是否启用alg控制功能
   algSwitch: False

enums/ModelTypeEnum-jcq.py

@@ -0,0 +1,768 @@
+import sys
+from enum import Enum, unique
+
+from common.Constant import COLOR
+
+sys.path.extend(['..', '../AIlib2'])
+from DMPR import DMPRModel
+from DMPRUtils.jointUtil import dmpr_yolo
+from segutils.segmodel import SegModel
+from utilsK.queRiver import riverDetSegMixProcess
+from utilsK.crowdGather import  gather_post_process
+from segutils.trafficUtils import tracfficAccidentMixFunction
+from utilsK.drownUtils import mixDrowing_water_postprocess
+from utilsK.noParkingUtils import mixNoParking_road_postprocess
+from utilsK.illParkingUtils import illParking_postprocess
+from stdc import stdcModel
+from yolov5 import yolov5Model
+from DMPRUtils.jointUtil import  dmpr_yolo_stdc
+from AI import default_mix
+from ocr import ocrModel
+from utilsK.channel2postUtils import channel2_post_process
+
+'''
+参数说明
+1. 编号  
+2. 模型编号
+3. 模型名称
+4. 选用的模型名称
+5. 模型配置
+6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
+'''
+
+
+@unique
+class ModelType(Enum):
+    WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
+        'seg_nclass': 2,
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'segRegionCnt': 1,
+        'segPar': {
+            'modelSize': (640, 360),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': riverDetSegMixProcess,
+                'pars': {
+                    'slopeIndex': [5, 6, 7],
+                    'riverIou': 0.1
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
+        'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+    
+    # FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
+        # 'device': device,
+        # 'gpu_name': gpuName,
+        # 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
+        # 'trtFlag_det': True,
+        # 'trtFlag_seg': False,
+        # 'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
+        # 'seg_nclass': 2,
+        # 'segRegionCnt': 0,
+        # 'slopeIndex': [],
+        # 'segPar': None,
+        # 'postFile': {
+            # "name": "post_process",
+            # "conf_thres": 0.25,
+            # "iou_thres": 0.45,
+            # "classes": 6,
+            # "rainbows": COLOR
+        # },
+        # 'Segweights': None
+    # })
+   
+
+    FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
+        'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
+        'postProcess':{'function':default_mix,'pars':{}},
+        'models':
+                [
+                    {
+                        'weight':"../AIlib2/weights/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+                        'name':'yolov5',
+                        'model':yolov5Model,
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
+                    }
+                    
+                        
+                ],
+                
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
+        'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+        "pixScale": 1.2,
+
+ 
+    })
+     
+
+
+    TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
+        'device': str(device),
+        'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故"],
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'seg_nclass': 3,
+        'segRegionCnt': 2,
+        'segPar': {
+            'modelSize': (640, 360),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'predResize': True,
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': tracfficAccidentMixFunction,
+                'pars': {
+                    'modelSize': (640, 360),
+                    #'modelSize': (1920,1080),
+                    'RoadArea': 16000,
+                    'roadVehicleAngle': 15,
+                    'speedRoadVehicleAngleMax': 75,
+                    'roundness': 1.0,
+                    'cls': 9,
+                    'vehicleFactor': 0.1,
+                    'confThres': 0.25,
+                    'roadIou': 0.6,
+                    'radius': 50,
+                    'vehicleFlag': False,
+                    'distanceFlag': False
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.25,
+            "classes": 10,
+            "rainbows": COLOR
+        },
+        'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
+        'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+
+    EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
+
+    PLATE_MODEL = ("5", "005", "车牌模型", None, None)
+
+    VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["车辆"],
+        'seg_nclass': 2,
+        'segRegionCnt': 0,
+        'slopeIndex': [],
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["行人"],
+        'seg_nclass': 2,
+        'segRegionCnt': 0,
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
+        'slopeIndex': [],
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["火焰", "烟雾"],
+        'seg_nclass': 2,  # 分割模型类别数目，默认2类
+        'segRegionCnt': 0,
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
+        'slopeIndex': [],
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["钓鱼", "游泳"],
+        'seg_nclass': 2,  # 分割模型类别数目，默认2类
+        'segRegionCnt': 0,
+        'slopeIndex': [],
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["违法种植"],
+        'seg_nclass': 2,  # 分割模型类别数目，默认2类
+        'segRegionCnt': 0,
+        'slopeIndex': [],
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
+        'model_size': (608, 608),
+        'K': 100,
+        'conf_thresh': 0.18,
+        'device': 'cuda:%s' % device,
+        'down_ratio': 4,
+        'num_classes': 15,
+        'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName,
+        'dataset': 'dota',
+        'half': False,
+        'mean': (0.5, 0.5, 0.5),
+        'std': (1, 1, 1),
+        'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
+        'decoder': None,
+        'test_flag': True,
+        "rainbows": COLOR,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'drawBox': False,
+        'label_array': None,
+        'labelnames': ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"),
+    })
+
+    BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)
+
+    CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["人"],
+        'seg_nclass': 2,  # 分割模型类别数目，默认2类
+        'segRegionCnt': 0,
+        'slopeIndex': [],
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
+                       "蓝藻"],
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'seg_nclass': 2,
+        'segRegionCnt': 1,
+        'segPar': {
+            'modelSize': (640, 360),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': riverDetSegMixProcess,
+                'pars': {
+                    'slopeIndex': [1, 3, 4, 7],
+                    'riverIou': 0.1
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.3,
+            "ovlap_thres_crossCategory": 0.65,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        # "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
+        'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
+        # '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
+        'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+
+    CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
+        'labelnames': ["车辆", "垃圾", "商贩", "违停"],
+        'postProcess':{
+                    'function':dmpr_yolo_stdc,
+                    'pars':{'carCls':0 ,'illCls':3,'scaleRatio':0.5,'border':80,'rubCls': 1, 'Rubfilter': 150}
+                    },
+        'models':[
+            {
+                #'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
+                'weight':'../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
+                'name':'yolov5',
+                'model':yolov5Model,
+                'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.5,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5  } }
+            },
+            {
+                'weight':'../AIlib2/weights/conf/cityMangement3/dmpr.pth',
+                'par':{
+                    'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
+                    'name':'dmpr'
+                        },
+                'model':DMPRModel,     
+                'name':'dmpr'
+            },
+            {
+                'weight':'../AIlib2/weights/conf/cityMangement3/stdc_360X640.pth',
+
+                'par':{
+                    'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,'seg_nclass':2},###分割模型预处理参数
+                'model':stdcModel,
+                'name':'stdc'           
+            } 
+        ],
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.5,
+            "iou_thres": 0.5,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
+        'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+        "pixScale": 1.2,
+    })
+
+    DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["人头", "人", "船只"],
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'seg_nclass': 2,
+        'segRegionCnt': 2,
+        'segPar': {
+            'modelSize': (640, 360),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'predResize': True,
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': mixDrowing_water_postprocess,
+                'pars': {
+                    'modelSize': (640, 360)
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.25,
+            "classes": 9,
+            "rainbows": COLOR
+        },
+        # "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
+        'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
+        # '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
+        'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+
+    NOPARKING_MODEL = (
+        "18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
+            'device': device,
+            'labelnames': ["车辆", "违停"],
+            'trtFlag_seg': True,
+            'trtFlag_det': True,
+            'seg_nclass': 4,
+            'segRegionCnt': 2,
+            'segPar': {
+                'modelSize': (640, 360),
+                'mean': (0.485, 0.456, 0.406),
+                'std': (0.229, 0.224, 0.225),
+                'predResize': True,
+                'numpy': False,
+                'RGB_convert_first': True,  ###分割模型预处理参数
+                'mixFunction': {
+                    'function': mixNoParking_road_postprocess,
+                    'pars': {
+                        'modelSize': (640, 360),
+                        'roundness': 0.3,
+                        'cls': 9,
+                        'laneArea': 10,
+                        'laneAngleCha': 5,
+                        'RoadArea': 16000,
+                        'fitOrder':2
+                    }
+                }
+            },
+            'postFile': {
+                "name": "post_process",
+                "conf_thres": 0.25,
+                "iou_thres": 0.25,
+                "classes": 9,
+                "rainbows": COLOR
+            },
+            'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
+            'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName
+        })
+
+    ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["车", "T角点", "L角点", "违停"],
+        'trtFlag_seg': False,
+        'trtFlag_det': True,
+        'seg_nclass': 4,
+        'segRegionCnt': 2,
+        'segPar': {
+            'mixFunction': {
+                'function': illParking_postprocess,
+                'pars': {}
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.25,
+            "classes": 9,
+            "rainbows": COLOR
+        },
+        'Detweights': "../AIlib2/weights/illParking/yolov5_%s_fp16.engine" % gpuName,
+        'Segweights': None
+    })
+
+    CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["护栏", "交通标志", "非交通标志", "施工", "施工"],
+        'trtFlag_seg': False,
+        'trtFlag_det': True,
+        'slopeIndex': [],
+        'seg_nclass': 2,
+        'segRegionCnt': 0,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.5,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Detweights': "../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine" % gpuName,
+        'Segweights': None
+    })
+
+    POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["坑槽"],
+        'seg_nclass': 2,  # 分割模型类别数目，默认2类
+        'segRegionCnt': 0,
+        'slopeIndex': [],
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/pothole/yolov5_%s_fp16.engine" % gpuName,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None,
+    })
+
+    CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+
+        'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只","未封仓"], # 保持原来的标签顺序不变，方便后面业务端增加
+        'segRegionCnt': 0,
+        'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
+            'objs':[2],
+            'wRation':1/6.0,
+            'hRation':1/6.0,
+            'smallId':0, #旗帜
+            'bigId':3,  #船只
+            'newId':4,  #未挂国旗船只
+            'uncoverId':5,  #未封仓标签
+            'recScale':1.2,
+            'target_cls':3.0, #目标种类
+            'filter_cls':4.0 #被过滤的种类
+            }},       
+        'models':[
+        {
+            #'weight':'../AIlib2/weights/conf/channel2/yolov5.pt',
+            # 'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
+            
+            'weight':'/home/thsw2/jcq/test/AIlib2/weights/channel2/best.pt', # yolov5 原来模型基础上增加了未封仓
+            
+            # 'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
+            
+            'name':'yolov5',
+            'model':yolov5Model,
+            'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
+        },
+        {                      
+            # 'weight' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',    
+            'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
+            'name':'ocr',
+            'model':ocrModel,
+            'par':{
+                'char_file':'../AIlib2/weights/conf/ocr2/benchmark.txt',
+                'mode':'ch',
+                'nc':3,
+                'imgH':32,
+                'imgW':192,
+                'hidden':256,
+                'mean':[0.5,0.5,0.5],
+                'std':[0.5,0.5,0.5],
+                'dynamic':False,
+                },
+        } ,
+        
+        
+        # {
+        #     'weight':'/home/thsw2/jcq/test/AIlib2/weights1/conf/channel2/yolov5_04.pt', # yolov5_04 添加了uncover 0 4 ；标签  yolov5_jcq
+        #     'name':'yolov5',
+        #     'model':yolov5Model,
+        #     'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.15,'iou_thres':0.25,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
+        # }
+
+         
+        ],
+        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3]],
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None,
+    })
+    
+    RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
+                       "蓝藻"],
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'seg_nclass': 2,
+        'segRegionCnt': 1,
+        'segPar': {
+            'modelSize': (640, 360),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': riverDetSegMixProcess,
+                'pars': {
+                    'slopeIndex': [1, 3, 4, 7],
+                    'riverIou': 0.1
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.3,
+            "ovlap_thres_crossCategory": 0.65,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        # "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
+        'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
+        # '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
+        'Segweights': '../AIlib2/weights/riverT/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+
+
+
+    FORESTCROWD_FARM_MODEL = ("2", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
+        'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
+        'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
+        'models':
+                [
+                    {
+                        'weight':"../AIlib2/weights/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+                        'name':'yolov5',
+                        'model':yolov5Model,
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.5,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{  "0":0.25,"1":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6   } },
+                    }
+
+
+                ],
+
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
+        'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+        "pixScale": 1.2,
+
+
+    })
+    TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
+        'device': str(device),
+        'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'seg_nclass': 3,
+        'segRegionCnt': 2,
+        'segPar': {
+            'modelSize': (640, 360),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'predResize': True,
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': tracfficAccidentMixFunction,
+                'pars': {
+                    'modelSize': (640, 360),
+                    #'modelSize': (1920,1080),
+                    'RoadArea': 16000,
+                    'roadVehicleAngle': 15,
+                    'speedRoadVehicleAngleMax': 75,
+                    'roundness': 1.0,
+                    'cls': 9,
+                    'vehicleFactor': 0.1,
+                    'confThres': 0.25,
+                    'roadIou': 0.6,
+                    'radius': 50,
+                    'vehicleFlag': False,
+                    'distanceFlag': False
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.25,
+            "classes": 10,
+            "rainbows": COLOR
+        },
+        'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
+        'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+
+
+
+    @staticmethod
+    def checkCode(code):
+        for model in ModelType:
+            if model.value[1] == code:
+                return True
+        return False
+
+
+'''
+    参数1: 检测目标名称
+    参数2: 检测目标
+    参数3: 初始化百度检测客户端
+'''
+
+
+@unique
+class BaiduModelTarget(Enum):
+    VEHICLE_DETECTION = (
+        "车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))
+
+    HUMAN_DETECTION = (
+        "人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))
+
+    PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))
+
+
+BAIDU_MODEL_TARGET_CONFIG = {
+    BaiduModelTarget.VEHICLE_DETECTION.value[1]: BaiduModelTarget.VEHICLE_DETECTION,
+    BaiduModelTarget.HUMAN_DETECTION.value[1]: BaiduModelTarget.HUMAN_DETECTION,
+    BaiduModelTarget.PEOPLE_COUNTING.value[1]: BaiduModelTarget.PEOPLE_COUNTING
+}
+
+EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}
+
+
+# 模型分析方式
+@unique
+class ModelMethodTypeEnum(Enum):
+    # 方式一: 正常识别方式
+    NORMAL = 1
+
+    # 方式二: 追踪识别方式
+    TRACE = 2

enums/ModelTypeEnum-raw.py

@@ -0,0 +1,807 @@
+import sys
+from enum import Enum, unique
+
+from common.Constant import COLOR
+
+sys.path.extend(['..', '../AIlib2'])
+from DMPR import DMPRModel
+from DMPRUtils.jointUtil import dmpr_yolo
+from segutils.segmodel import SegModel
+from utilsK.queRiver import riverDetSegMixProcess
+from utilsK.crowdGather import  gather_post_process
+from segutils.trafficUtils import tracfficAccidentMixFunction
+from utilsK.drownUtils import mixDrowing_water_postprocess
+from utilsK.noParkingUtils import mixNoParking_road_postprocess
+from utilsK.illParkingUtils import illParking_postprocess
+from stdc import stdcModel
+from yolov5 import yolov5Model
+from DMPRUtils.jointUtil import  dmpr_yolo_stdc
+from AI import default_mix
+from ocr import ocrModel
+from utilsK.channel2postUtils import channel2_post_process
+
+'''
+参数说明
+1. 编号  
+2. 模型编号
+3. 模型名称
+4. 选用的模型名称
+5. 模型配置
+6. 模型引用配置[Detweights文件, Segweights文件, 引用计数]
+'''
+
+
+@unique
+class ModelType(Enum):
+    WATER_SURFACE_MODEL = ("1", "001", "河道模型", 'river', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["排口", "水生植被", "其它", "漂浮物", "污染排口", "菜地", "违建", "岸坡垃圾"],
+        'seg_nclass': 2,
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'segRegionCnt': 1,
+        'segPar': {
+            'modelSize': (640, 360),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': riverDetSegMixProcess,
+                'pars': {
+                    'slopeIndex': [5, 6, 7],
+                    'riverIou': 0.1
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Detweights': "../AIlib2/weights/river/yolov5_%s_fp16.engine" % gpuName,
+        'Segweights': '../AIlib2/weights/river/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+    
+    # FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
+        # 'device': device,
+        # 'gpu_name': gpuName,
+        # 'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
+        # 'trtFlag_det': True,
+        # 'trtFlag_seg': False,
+        # 'Detweights': "../AIlib2/weights/forest2/yolov5_%s_fp16.engine" % gpuName,
+        # 'seg_nclass': 2,
+        # 'segRegionCnt': 0,
+        # 'slopeIndex': [],
+        # 'segPar': None,
+        # 'postFile': {
+            # "name": "post_process",
+            # "conf_thres": 0.25,
+            # "iou_thres": 0.45,
+            # "classes": 6,
+            # "rainbows": COLOR
+        # },
+        # 'Segweights': None
+    # })
+   
+
+    FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
+        'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","云朵"],
+        'postProcess':{'function':default_mix,'pars':{}},
+        'models':
+                [
+                    {
+                        'weight':"../AIlib2/weights/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+                        'name':'yolov5',
+                        'model':yolov5Model,
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
+                    }
+                    
+                        
+                ],
+                
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
+        'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+        "pixScale": 1.2,
+
+ 
+    })
+     
+
+
+    TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
+        'device': str(device),
+        'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故"],
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'seg_nclass': 3,
+        'segRegionCnt': 2,
+        'segPar': {
+            #'modelSize': (640, 360),
+            'modelSize': (1920, 1080),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'predResize': True,
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': tracfficAccidentMixFunction,
+                'pars': {
+                    #'modelSize': (640, 360),
+                    'modelSize': (1920,1080),
+                    'RoadArea': 16000,
+                    'roadVehicleAngle': 15,
+                    'speedRoadVehicleAngleMax': 75,
+                    'roundness': 1.0,
+                    'cls': 10,
+                    'vehicleFactor': 0.1,
+                    'confThres': 0.25,
+                    'roadIou': 0.6,
+                    'radius': 50,
+                    'vehicleFlag': False,
+                    'distanceFlag': False
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.25,
+            "classes": 10,
+            "rainbows": COLOR
+        },
+        'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
+        'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+
+    EPIDEMIC_PREVENTION_MODEL = ("4", "004", "防疫模型", None, None)
+
+    PLATE_MODEL = ("5", "005", "车牌模型", None, None)
+
+    VEHICLE_MODEL = ("6", "006", "车辆模型", 'vehicle', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["车辆"],
+        'seg_nclass': 2,
+        'segRegionCnt': 0,
+        'slopeIndex': [],
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/vehicle/yolov5_%s_fp16.engine" % gpuName,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["行人"],
+        'seg_nclass': 2,
+        'segRegionCnt': 0,
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/pedestrian/yolov5_%s_fp16.engine" % gpuName,
+        'slopeIndex': [],
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["火焰", "烟雾"],
+        'seg_nclass': 2,  # 分割模型类别数目，默认2类
+        'segRegionCnt': 0,
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/smogfire/yolov5_%s_fp16.engine" % gpuName,
+        'slopeIndex': [],
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["钓鱼", "游泳"],
+        'seg_nclass': 2,  # 分割模型类别数目，默认2类
+        'segRegionCnt': 0,
+        'slopeIndex': [],
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/AnglerSwimmer/yolov5_%s_fp16.engine" % gpuName,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["违法种植"],
+        'seg_nclass': 2,  # 分割模型类别数目，默认2类
+        'segRegionCnt': 0,
+        'slopeIndex': [],
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/countryRoad/yolov5_%s_fp16.engine" % gpuName,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
+        'model_size': (608, 608),
+        'K': 100,
+        'conf_thresh': 0.18,
+        'device': 'cuda:%s' % device,
+        'down_ratio': 4,
+        'num_classes': 15,
+        'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName,
+        'dataset': 'dota',
+        'half': False,
+        'mean': (0.5, 0.5, 0.5),
+        'std': (1, 1, 1),
+        'heads': {'hm': None, 'wh': 10, 'reg': 2, 'cls_theta': 1},
+        'decoder': None,
+        'test_flag': True,
+        "rainbows": COLOR,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'drawBox': False,
+        'label_array': None,
+        'labelnames': ("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"),
+    })
+
+    BAIDU_MODEL = ("12", "012", "百度AI图片识别模型", None, None)
+
+    CHANNEL_EMERGENCY_MODEL = ("13", "013", "航道模型", 'channelEmergency', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["人"],
+        'seg_nclass': 2,  # 分割模型类别数目，默认2类
+        'segRegionCnt': 0,
+        'slopeIndex': [],
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine" % gpuName,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None
+    })
+
+    RIVER2_MODEL = ("15", "015", "河道检测模型", 'river2', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
+                       "蓝藻"],
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'seg_nclass': 2,
+        'segRegionCnt': 1,
+        'segPar': {
+            'modelSize': (640, 360),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': riverDetSegMixProcess,
+                'pars': {
+                    'slopeIndex': [1, 3, 4, 7],
+                    'riverIou': 0.1
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.3,
+            "ovlap_thres_crossCategory": 0.65,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        # "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
+        'Detweights': "../AIlib2/weights/river2/yolov5_%s_fp16.engine" % gpuName,
+        # '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
+        'Segweights': '../AIlib2/weights/river2/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+
+    CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
+        'labelnames': ["车辆", "垃圾", "商贩", "裸土","占道经营","违停"],
+        'postProcess':{
+                    'function':dmpr_yolo_stdc,
+                    'pars':{'carCls':0 ,'illCls':5,'scaleRatio':0.5,'border':80}
+                    },
+        'models':[
+            {
+                #'weight':'../AIlib2/weights/conf/cityMangement3/yolov5.pt',
+                'weight':'../AIlib2/weights/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
+                'name':'yolov5',
+                'model':yolov5Model,
+                'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3,4,5],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5,"4":0.4,"5":0.5  } }
+            },
+            {
+                'weight':'../AIlib2/weights/conf/cityMangement3/dmpr.pth',
+                'par':{
+                    'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
+                    'name':'dmpr'
+                        },
+                'model':DMPRModel,     
+                'name':'dmpr'
+            },
+            {
+                'weight':'../AIlib2/weights/conf/cityMangement3/stdc_360X640.pth',
+                'par':{
+                    'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,'seg_nclass':2},###分割模型预处理参数
+                'model':stdcModel,
+                'name':'stdc'           
+            } 
+        ],
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
+        'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+        "pixScale": 1.2,
+    })
+
+    DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["人头", "人", "船只"],
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'seg_nclass': 2,
+        'segRegionCnt': 2,
+        'segPar': {
+            'modelSize': (640, 360),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'predResize': True,
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': mixDrowing_water_postprocess,
+                'pars': {
+                    'modelSize': (640, 360)
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.25,
+            "classes": 9,
+            "rainbows": COLOR
+        },
+        # "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
+        'Detweights': "../AIlib2/weights/drowning/yolov5_%s_fp16.engine" % gpuName,
+        # '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
+        'Segweights': '../AIlib2/weights/drowning/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+
+    NOPARKING_MODEL = (
+        "18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
+            'device': device,
+            'labelnames': ["车辆", "违停"],
+            'trtFlag_seg': True,
+            'trtFlag_det': True,
+            'seg_nclass': 4,
+            'segRegionCnt': 2,
+            'segPar': {
+                'modelSize': (640, 360),
+                'mean': (0.485, 0.456, 0.406),
+                'std': (0.229, 0.224, 0.225),
+                'predResize': True,
+                'numpy': False,
+                'RGB_convert_first': True,  ###分割模型预处理参数
+                'mixFunction': {
+                    'function': mixNoParking_road_postprocess,
+                    'pars': {
+                        'modelSize': (640, 360),
+                        'roundness': 0.3,
+                        'cls': 9,
+                        'laneArea': 10,
+                        'laneAngleCha': 5,
+                        'RoadArea': 16000,
+                        'fitOrder':2
+                    }
+                }
+            },
+            'postFile': {
+                "name": "post_process",
+                "conf_thres": 0.25,
+                "iou_thres": 0.25,
+                "classes": 9,
+                "rainbows": COLOR
+            },
+            'Detweights': "../AIlib2/weights/noParking/yolov5_%s_fp16.engine" % gpuName,
+            'Segweights': '../AIlib2/weights/noParking/stdc_360X640_%s_fp16.engine' % gpuName
+        })
+
+    ILLPARKING_MODEL = ("19", "019", "车辆违停模型", 'illParking', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["车", "T角点", "L角点", "违停"],
+        'trtFlag_seg': False,
+        'trtFlag_det': True,
+        'seg_nclass': 4,
+        'segRegionCnt': 2,
+        'segPar': {
+            'mixFunction': {
+                'function': illParking_postprocess,
+                'pars': {}
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.25,
+            "classes": 9,
+            "rainbows": COLOR
+        },
+        'Detweights': "../AIlib2/weights/illParking/yolov5_%s_fp16.engine" % gpuName,
+        'Segweights': None
+    })
+
+    CITYROAD_MODEL = ("20", "020", "城市公路模型", 'cityRoad', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["护栏", "交通标志", "非交通标志", "施工", "施工"],
+        'trtFlag_seg': False,
+        'trtFlag_det': True,
+        'slopeIndex': [],
+        'seg_nclass': 2,
+        'segRegionCnt': 0,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.8,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Detweights': "../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine" % gpuName,
+        'Segweights': None
+    })
+
+    POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["坑槽"],
+        'seg_nclass': 2,  # 分割模型类别数目，默认2类
+        'segRegionCnt': 0,
+        'slopeIndex': [],
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../AIlib2/weights/pothole/yolov5_%s_fp16.engine" % gpuName,
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None,
+    })
+
+    CHANNEL2_MODEL = ("24", "024", "船只综合检测模型", 'channel2', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["国旗", "浮标", "船名", "船只","未挂国旗船只"],
+        'segRegionCnt': 0,
+        'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{
+            'objs':[2],
+            'wRation':1/6.0,
+            'hRation':1/6.0,
+            'smallId':0,
+            'bigId':3,
+            'newId':4,
+            'recScale':1.2}},       
+        'models':[
+        {
+            #'weight':'../AIlib2/weights/conf/channel2/yolov5.pt',
+            'weight':'../AIlib2/weights/channel2/yolov5_%s_fp16.engine'%(gpuName),
+            'name':'yolov5',
+            'model':yolov5Model,
+            'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
+        },
+        {                      
+            # 'weight' : '../AIlib2/weights/ocr2/crnn_ch_4090_fp16_192X32.engine',    
+            'weight' : '../AIlib2/weights/conf/ocr2/crnn_ch.pth',
+            'name':'ocr',
+            'model':ocrModel,
+            'par':{
+                'char_file':'../AIlib2/weights/conf/ocr2/benchmark.txt',
+                'mode':'ch',
+                'nc':3,
+                'imgH':32,
+                'imgW':192,
+                'hidden':256,
+                'mean':[0.5,0.5,0.5],
+                'std':[0.5,0.5,0.5],
+                'dynamic':False,
+                },
+        }  
+        ],
+        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3]],
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None,
+    })
+    
+    RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
+        'device': device,
+        'labelnames': ["漂浮物", "岸坡垃圾", "排口", "违建", "菜地", "水生植物", "河湖人员", "钓鱼人员", "船只",
+                       "蓝藻"],
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'seg_nclass': 2,
+        'segRegionCnt': 1,
+        'segPar': {
+            'modelSize': (640, 360),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': riverDetSegMixProcess,
+                'pars': {
+                    'slopeIndex': [1, 3, 4, 7],
+                    'riverIou': 0.1
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.3,
+            "ovlap_thres_crossCategory": 0.65,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        # "../AIlib2/weights/conf/%s/yolov5.pt" % modeType.value[3]
+        'Detweights': "../AIlib2/weights/riverT/yolov5_%s_fp16.engine" % gpuName,
+        # '../AIlib2/weights/conf/%s/stdc_360X640.pth' % modeType.value[3]
+        'Segweights': '../AIlib2/weights/riverT/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+
+
+
+    FORESTCROWD_FARM_MODEL = ("26", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
+        'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
+        'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
+        'models':
+                [
+                    {
+                        'weight':"../AIlib2/weights/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+                        'name':'yolov5',
+                        'model':yolov5Model,
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{  "0":0.25,"1":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6   } },
+                    }
+
+
+                ],
+
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
+        'segRegionCnt':2,###分割模型结果需要保留的等值线数目
+        "pixScale": 1.2,
+
+
+    })
+    TRAFFICFORDSJ_FARM_MODEL = ("27", "027", "交通模型-大数据局", 'highWay2T', lambda device, gpuName: {
+        'device': str(device),
+        'labelnames': ["行人", "车辆", "纵向裂缝", "横向裂缝", "修补", "网状裂纹", "坑槽", "块状裂纹", "积水", "影子", "事故", "桥梁外观","设施破损缺失","龙门架","防抛网","标识牌损坏","护栏损坏","钢筋裸露" ],
+        'trtFlag_seg': True,
+        'trtFlag_det': True,
+        'seg_nclass': 3,
+        'segRegionCnt': 2,
+        'segPar': {
+            'modelSize': (640, 360),
+            'mean': (0.485, 0.456, 0.406),
+            'std': (0.229, 0.224, 0.225),
+            'predResize': True,
+            'numpy': False,
+            'RGB_convert_first': True,
+            'mixFunction': {
+                'function': tracfficAccidentMixFunction,
+                'pars': {
+                    'modelSize': (640, 360),
+                    #'modelSize': (1920,1080),
+                    'RoadArea': 16000,
+                    'roadVehicleAngle': 15,
+                    'speedRoadVehicleAngleMax': 75,
+                    'roundness': 1.0,
+                    'cls': 9,
+                    'vehicleFactor': 0.1,
+                    'confThres': 0.25,
+                    'roadIou': 0.6,
+                    'radius': 50,
+                    'vehicleFlag': False,
+                    'distanceFlag': False
+                }
+            }
+        },
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.25,
+            "classes": 10,
+            "rainbows": COLOR
+        },
+        'Detweights': "../AIlib2/weights/highWay2/yolov5_%s_fp16.engine" % gpuName,
+        'Segweights': '../AIlib2/weights/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
+    })
+
+    SMARTSITE_MODEL = ("28", "028", "智慧工地模型", 'smartSite', lambda device, gpuName: {
+        'labelnames': [  "工人","塔式起重机","悬臂","起重机","压路机","推土机","挖掘机","卡车","装载机","泵车","混凝土搅拌车","打桩","其他车辆" ],
+        'postProcess':{'function':default_mix,'pars':{}},
+        'models':
+                [
+                    {
+                        'weight':"../AIlib2/weights/smartSite/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+                        'name':'yolov5',
+                        'model':yolov5Model,
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },         
+                    }
+                    
+                        
+                ],
+        'postFile': {
+            "rainbows": COLOR
+        },
+        
+    })
+
+    RUBBISH_MODEL = ("29", "029", "垃圾模型", 'rubbish', lambda device, gpuName: {
+        'labelnames': [ "建筑垃圾","白色垃圾","其他垃圾"],
+        'postProcess':{'function':default_mix,'pars':{}},
+        'models':
+                [
+                    {
+                        'weight':"../AIlib2/weights/rubbish/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+                        'name':'yolov5',
+                        'model':yolov5Model,
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },         
+                    }
+                    
+                        
+                ],
+        'postFile': {
+            "rainbows": COLOR
+        },
+        
+    })
+    
+    FIREWORK_MODEL = ("30", "030", "烟花模型", 'firework', lambda device, gpuName: {
+        'labelnames': [ "烟花"],
+        'postProcess':{'function':default_mix,'pars':{}},
+        'models':
+                [
+                    {
+                        'weight':"../AIlib2/weights/firework/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
+                        'name':'yolov5',
+                        'model':yolov5Model,
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },         
+                    }
+                    
+                        
+                ],
+        'postFile': {
+            "rainbows": COLOR
+        },
+        
+    })
+    
+
+    @staticmethod
+    def checkCode(code):
+        for model in ModelType:
+            if model.value[1] == code:
+                return True
+        return False
+
+
+'''
+    参数1: 检测目标名称
+    参数2: 检测目标
+    参数3: 初始化百度检测客户端
+'''
+
+
+@unique
+class BaiduModelTarget(Enum):
+    VEHICLE_DETECTION = (
+        "车辆检测", 0, lambda client0, client1, url, request_id: client0.vehicleDetectUrl(url, request_id))
+
+    HUMAN_DETECTION = (
+        "人体检测与属性识别", 1, lambda client0, client1, url, request_id: client1.bodyAttr(url, request_id))
+
+    PEOPLE_COUNTING = ("人流量统计", 2, lambda client0, client1, url, request_id: client1.bodyNum(url, request_id))
+
+
+BAIDU_MODEL_TARGET_CONFIG = {
+    BaiduModelTarget.VEHICLE_DETECTION.value[1]: BaiduModelTarget.VEHICLE_DETECTION,
+    BaiduModelTarget.HUMAN_DETECTION.value[1]: BaiduModelTarget.HUMAN_DETECTION,
+    BaiduModelTarget.PEOPLE_COUNTING.value[1]: BaiduModelTarget.PEOPLE_COUNTING
+}
+
+EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}
+
+
+# 模型分析方式
+@unique
+class ModelMethodTypeEnum(Enum):
+    # 方式一: 正常识别方式
+    NORMAL = 1
+
+    # 方式二: 追踪识别方式
+    TRACE = 2

enums/ModelTypeEnum.py

@@ -14,6 +14,7 @@ from utilsK.drownUtils import mixDrowing_water_postprocess
 from utilsK.noParkingUtils import mixNoParking_road_postprocess
 from utilsK.illParkingUtils import illParking_postprocess
 from utilsK.pannelpostUtils import pannel_post_process
+from utilsK.securitypostUtils import security_post_process
 from stdc import stdcModel
 from yolov5 import yolov5Model
 from p2pNet import p2NnetModel
@@ -63,7 +64,7 @@ class ModelType(Enum):
             "classes": 5,
             "rainbows": COLOR
         },
-
+        'fiterList':[2],
         'Detweights': "../weights/trt/AIlib2/river/yolov5_%s_fp16.engine" % gpuName,
         'Segweights': '../weights/trt/AIlib2/river/stdc_360X640_%s_fp16.engine' % gpuName
     })
@@ -99,10 +100,8 @@ class ModelType(Enum):
                         'weight':"../weights/trt/AIlib2/forest2/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
                         'name':'yolov5',
                         'model':yolov5Model,
-                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False},
                     }
-
-
                 ],
 
         'postFile': {
@@ -112,11 +111,10 @@ class ModelType(Enum):
             "classes": 5,
             "rainbows": COLOR
         },
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
+        "score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3},
+        'fiterList': [5],
         'segRegionCnt':2,###分割模型结果需要保留的等值线数目
         "pixScale": 1.2,
-
-
     })
 
 
@@ -162,7 +160,8 @@ class ModelType(Enum):
             "classes": 10,
             "rainbows": COLOR
         },
-        'allowedList':[0,1,2,3,4,5,6,7,8,9,10,11,12,16,17,18,19,20,21,22],
+        'score_byClass':{11:0.75,12:0.75},
+        'fiterList': [13,14,15,16,17,18,19,20,21,22],
         'Detweights': "../weights/trt/AIlib2/highWay2/yolov5_%s_fp16.engine" % gpuName,
         'Segweights': '../weights/trt/AIlib2/highWay2/stdc_360X640_%s_fp16.engine' % gpuName
     })
@@ -231,7 +230,7 @@ class ModelType(Enum):
             "classes": 5,
             "rainbows": COLOR
         },
-        'Segweights': None
+        'Segweights': None,
     })
 
     ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
@@ -345,7 +344,8 @@ class ModelType(Enum):
                 'function': riverDetSegMixProcess,
                 'pars': {
                     'slopeIndex': [1, 3, 4, 7],
-                    'riverIou': 0.1
+                    'riverIou': 0.1,
+                    'scale': 0.25
                 }
             }
         },
@@ -374,13 +374,14 @@ class ModelType(Enum):
                     },
         'models':[
             {
-                'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
-                'name':'yolov5',
+                 'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
+                 'name':'yolov5',
                 'model':yolov5Model,
-                'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3,4,5,6,7],'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':True, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5  } }
+                'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':True}
             },
             {
-                'weight':'../weights/pth/AIlib2/cityMangement3/dmpr.pth',
+                'weight':'../weights/trt/AIlib2/cityMangement3/dmpr_3090.engine',
+                #'weight':'../weights/pth/AIlib2/cityMangement3/dmpr.pth',
                 'par':{
                     'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
                     'name':'dmpr'
@@ -403,7 +404,7 @@ class ModelType(Enum):
             "classes": 8,
             "rainbows": COLOR
         },
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
+        "score_byClass":{0:0.8, 1:0.4, 2:0.5, 3:0.5},
         'segRegionCnt':2,###分割模型结果需要保留的等值线数目
         "pixScale": 1.2,
     })
@@ -568,10 +569,10 @@ class ModelType(Enum):
             'weight':'../weights/trt/AIlib2/channel2/yolov5_%s_fp16.engine'%(gpuName),
             'name':'yolov5',
             'model':yolov5Model,
-            'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
+            'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False}
         },
         {
-            'weight' : '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
+            'weight' : '../weights/trt/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(gpuName),
             'name':'ocr',
             'model':ocrModel,
             'par':{
@@ -587,7 +588,6 @@ class ModelType(Enum):
                 },
         }
         ],
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]],
         'segPar': None,
         'postFile': {
             "name": "post_process",
@@ -597,6 +597,8 @@ class ModelType(Enum):
             "rainbows": COLOR
         },
         'Segweights': None,
+        "score_byClass": {0: 0.7, 1: 0.7, 2: 0.8, 3: 0.6}
+        
     })
 
     RIVERT_MODEL = ("25", "025", "河道检测模型(T)", 'riverT', lambda device, gpuName: {
@@ -633,8 +635,6 @@ class ModelType(Enum):
         'Segweights': '../weights/trt/AIlib2/riverT/stdc_360X640_%s_fp16.engine' % gpuName
     })
 
-
-
     FORESTCROWD_FARM_MODEL = ("26", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
         'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
         'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
@@ -644,7 +644,7 @@ class ModelType(Enum):
                         'weight':"../weights/trt/AIlib2/forestCrowd/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
                         'name':'yolov5',
                         'model':yolov5Model,
-                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{  "0":0.25,"1":0.25,"2":0.6,"3":0.6,'4':0.6 ,'5':0.6   } },
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False},
                     }
 
 
@@ -657,7 +657,7 @@ class ModelType(Enum):
             "classes": 5,
             "rainbows": COLOR
         },
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
+        "score_byClass":{0:0.25,1:0.25,2:0.6,3:0.6,4:0.6 ,5:0.6},
         'segRegionCnt':2,###分割模型结果需要保留的等值线数目
         "pixScale": 1.2,
 
@@ -705,6 +705,7 @@ class ModelType(Enum):
             "classes": 10,
             "rainbows": COLOR
         },
+        'fiterltList': [11,12,13,14,15,16,17],
         'Detweights': "../weights/trt/AIlib2/highWay2T/yolov5_%s_fp16.engine" % gpuName,
         'Segweights': '../weights/trt/AIlib2/highWay2T/stdc_360X640_%s_fp16.engine' % gpuName
     })
@@ -718,7 +719,7 @@ class ModelType(Enum):
                         'weight':"../weights/trt/AIlib2/smartSite/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
                         'name':'yolov5',
                         'model':yolov5Model,
-                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },         
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False},
                     }
 
 
@@ -726,6 +727,7 @@ class ModelType(Enum):
         'postFile': {
             "rainbows": COLOR
         },
+        "score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3}
 
     })
 
@@ -738,7 +740,7 @@ class ModelType(Enum):
                         'weight':"../weights/trt/AIlib2/rubbish/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
                         'name':'yolov5',
                         'model':yolov5Model,
-                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },         
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False},
                     }
 
 
@@ -746,6 +748,7 @@ class ModelType(Enum):
         'postFile': {
             "rainbows": COLOR
         },
+        "score_byClass": {0: 0.25, 1: 0.3, 2: 0.3, 3: 0.3}
 
     })
 
@@ -758,7 +761,7 @@ class ModelType(Enum):
                         'weight':"../weights/trt/AIlib2/firework/yolov5_%s_fp16.engine"%(gpuName),###检测模型路径
                         'name':'yolov5',
                         'model':yolov5Model,
-                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },         
+                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':False },
                     }
 
 
@@ -766,7 +769,6 @@ class ModelType(Enum):
         'postFile': {
             "rainbows": COLOR
         },
-
     })
 
     TRAFFIC_SPILL_MODEL = ("50", "501", "高速公路抛洒物模型", 'highWaySpill', lambda device, gpuName: {
@@ -808,7 +810,7 @@ class ModelType(Enum):
             "classes": 2,
             "rainbows": COLOR
         },
-        'detModelpara': [{"id": str(x), "config": {"k1": "v1", "k2": "v2"}} for x in [0]],
+        'fiterList': [1],
         ###控制哪些检测类别显示、输出
         'Detweights': "../weights/trt/AIlib2/highWaySpill/yolov5_%s_fp16.engine" % gpuName,
         'Segweights': '../weights/trt/AIlib2/highWaySpill/stdc_360X640_%s_fp16.engine' % gpuName
@@ -853,7 +855,7 @@ class ModelType(Enum):
             "classes": 4,
             "rainbows": COLOR
         },
-        'detModelpara': [{"id": str(x), "config": {"k1": "v1", "k2": "v2"}} for x in [0]],
+        'fiterList':[1,2,3],
         ###控制哪些检测类别显示、输出
         'Detweights': "../weights/trt/AIlib2/highWayCthc/yolov5_%s_fp16.engine" % gpuName,
         'Segweights': '../weights/trt/AIlib2/highWayCthc/stdc_360X640_%s_fp16.engine' % gpuName
@@ -869,14 +871,15 @@ class ModelType(Enum):
                     'name': 'yolov5',
                     'model': yolov5Model,
                     'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.25, 'iou_thres': 0.45,
-                            'allowedList': [0,1,2], 'segRegionCnt': 1, 'trtFlag_det': True,
-                            'trtFlag_seg': False, "score_byClass": {"0": 0.25, "1": 0.3, "2": 0.3, "3": 0.3}},
+                            'segRegionCnt': 1, 'trtFlag_det': True,
+                            'trtFlag_seg': False},
                 }
 
             ],
         'postFile': {
             "rainbows": COLOR
         },
+        'fiterList':[0]
 
     })
 
@@ -886,25 +889,27 @@ class ModelType(Enum):
         'rainbows': COLOR,
         'models': [
             {
-                'trtFlag_det': False,
-                'weight': '../weights/pth/AIlib2/carplate/plate_yolov5s_v3.jit',
+                #'weight': '../weights/pth/AIlib2/carplate/plate_yolov5s_v3.jit',
+                'weight': '../weights/trt/AIlib2/carplate/yolov5_%s_fp16.engine' % (gpuName),
                 'name': 'yolov5',
                 'model': yolov5Model,
                 'par': {
+                    'trtFlag_det': True,
                     'device': 'cuda:0',
-                    'half': False,
+                    'half': True,
                     'conf_thres': 0.4,
                     'iou_thres': 0.45,
                     'nc': 1,
+                    'plate':8,
                     'plate_dilate': (0.5, 0.1)
                 },
             },
             {
-                'trtFlag_ocr': False,
-                'weight': '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
+                'weight' : '../weights/trt/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(gpuName),
                 'name': 'ocr',
                 'model': ocrModel,
                 'par': {
+                    'trtFlag_ocr': True,
                     'char_file': '../AIlib2/conf/ocr2/benchmark.txt',
                     'mode': 'ch',
                     'nc': 3,
@@ -928,10 +933,8 @@ class ModelType(Enum):
                     'name': 'yolov5',
                     'model': yolov5Model,
                     'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
-                            'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
-                            'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
+                             'segRegionCnt': 1, 'trtFlag_det': True,'trtFlag_seg': False},
                 }
-
             ],
         'postFile': {
             "rainbows": COLOR
@@ -949,8 +952,7 @@ class ModelType(Enum):
                     'name': 'yolov5',
                     'model': yolov5Model,
                     'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
-                            'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
-                            'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
+                            'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
                 }
 
             ],
@@ -976,12 +978,14 @@ class ModelType(Enum):
                     'row': 2,
                     'line': 2,
                     'point_loss_coef': 0.45,
-                    'conf': 0.25,
+                    'conf': 0.65,
                     'gpu_id': 0,
                     'eos_coef': '0.5',
                     'set_cost_class': 1,
                     'set_cost_point': 0.05,
-                    'backbone': 'vgg16_bn'
+                    'backbone': 'vgg16_bn',
+                    'expend': 10,
+                    'psize': 2,
                 },
             }],
     })
@@ -995,8 +999,7 @@ class ModelType(Enum):
                     'name': 'yolov5',
                     'model': yolov5Model,
                     'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
-                            'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
-                            'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
+                            'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
                 }
 
             ],
@@ -1006,6 +1009,89 @@ class ModelType(Enum):
 
     })
 
+    CITY_UNDERBUILDCOUNT_MODEL = ("30", "306", "建筑物下人群计数", 'perUnderBuild', lambda device, gpuName: {
+        'labelnames': ["建筑物下人群"],
+        'device': str(device),
+        'rainbows': COLOR,
+        'models': [
+            {
+                'weight': "../weights/trt/AIlib2/perUnderBuild/yolov5_%s_fp16.engine" % (gpuName),  ###检测模型路径
+                'name': 'yolov5',
+                'model': yolov5Model,
+                'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.25, 'iou_thres': 0.45,
+                        'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
+            },
+            {
+                'trtFlag_det': False,
+                'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth",  ###检测模型路径
+                'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth",  ###检测模型路径
+                'name': 'p2pnet',
+                'model': p2NnetModel,
+                'par': {
+                    'device': 'cuda:0',
+                    'row': 2,
+                    'line': 2,
+                    'point_loss_coef': 0.45,
+                    'conf': 0.50,
+                    'gpu_id': 0,
+                    'eos_coef': '0.5',
+                    'set_cost_class': 1,
+                    'set_cost_point': 0.05,
+                    'backbone': 'vgg16_bn',
+                    'expend': 10,
+                    'psize': 5
+                },
+            }],
+    })
+
+    CITY_FIREAREA_MODEL = ("30", "307", "火焰面积模型", 'FireArea', lambda device, gpuName: {
+        'device': device,
+        'gpu_name': gpuName,
+        'labelnames': ["火焰"],
+        'seg_nclass': 2,  # 分割模型类别数目，默认2类
+        'segRegionCnt': 0,
+        'trtFlag_det': True,
+        'trtFlag_seg': False,
+        'Detweights': "../weights/trt/AIlib2/smogfire/yolov5_%s_fp16.engine" % gpuName, # 0：fire  1：smoke
+        'Samweights': "../weights/pth/AIlib2/firearea/sam_vit_b_01ec64.pth", #分割模型
+        'ksize':(7,7),
+        'sam_type':'vit_b',
+        'slopeIndex': [],
+        'segPar': None,
+        'postFile': {
+            "name": "post_process",
+            "conf_thres": 0.25,
+            "iou_thres": 0.45,
+            "classes": 5,
+            "rainbows": COLOR
+        },
+        'Segweights': None,
+        'fiterList':[1],
+        "score_byClass": {0: 0.1}
+
+    })
+
+    CITY_SECURITY_MODEL = ("30", "308", "安防模型", 'SECURITY', lambda device, gpuName: {
+        'labelnames': ["带安全帽","安全帽","攀爬","斗殴","未戴安全帽"],
+        'postProcess': {'function': security_post_process, 'pars': {'objs': [0,1],'iou':0.25,'unhelmet':4}},
+        'models':
+            [
+                {
+                    'weight': "../weights/trt/AIlib2/security/yolov5_%s_fp16.engine" % (gpuName),  ###检测模型路径
+                    'name': 'yolov5',
+                    'model': yolov5Model,
+                    'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.25, 'iou_thres': 0.45,
+                            'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
+                }
+
+            ],
+        'postFile': {
+            "rainbows": COLOR
+        },
+        'fiterList': [0,1],
+        "score_byClass": {"0": 0.50}
+    })    
+
     @staticmethod
     def checkCode(code):
         for model in ModelType:

util/ModelUtils.py

@@ -27,6 +27,7 @@ import torch
 import tensorrt as trt
 from utilsK.jkmUtils import pre_process, post_process, get_return_data
 from DMPR import DMPRModel
+from segment_anything import SamPredictor, sam_model_registry
 FONT_PATH = "../AIlib2/conf/platech.ttf"
 
 
@@ -36,6 +37,7 @@ class OneModel:
 
     def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
         try:
+            start = time.time()
             logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
                         requestId)
             par = modeType.value[4](str(device), gpu_name)
@@ -46,8 +48,12 @@ class OneModel:
             new_device = select_device(par.get('device'))
             half = new_device.type != 'cpu'
             Detweights = par['Detweights']
-            with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
-                model = runtime.deserialize_cuda_engine(f.read())
+            if par['trtFlag_det']:
+                with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
+                    model = runtime.deserialize_cuda_engine(f.read())
+            else:
+                model = attempt_load(Detweights, map_location=new_device)  # load FP32 model
+                if half: model.half()
             par['segPar']['seg_nclass'] = par['seg_nclass']
             Segweights = par['Segweights']
             if Segweights:
@@ -64,10 +70,11 @@ class OneModel:
                 'ovlap_thres_crossCategory': postFile.get("ovlap_thres_crossCategory"),
                 'iou_thres': postFile["iou_thres"],
                 # 对高速模型进行过滤
-                'allowedList': par['allowedList'] if modeType.value[0] == '3' else [],
                 'segRegionCnt': par['segRegionCnt'],
                 'trtFlag_det': par['trtFlag_det'],
-                'trtFlag_seg': par['trtFlag_seg']
+                'trtFlag_seg': par['trtFlag_seg'],
+                'score_byClass':par['score_byClass'] if 'score_byClass' in par.keys() else None,
+                'fiterList': par['fiterList'] if 'fiterList' in par.keys() else []
             }
             model_param = {
                 "model": model,
@@ -82,6 +89,7 @@ class OneModel:
             logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
             raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                    ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+        logger.info("模型初始化时间：{}, requestId:{}", time.time() - start, requestId)
 # 纯分类模型
 class cityManagementModel:
     __slots__ = "model_conf"
@@ -99,6 +107,8 @@ class cityManagementModel:
             model_param = {
                 "modelList": modelList,
                 "postProcess": postProcess,
+                "score_byClass":par['score_byClass'] if 'score_byClass' in par.keys() else None,
+                "fiterList":par['fiterList'] if 'fiterList' in par.keys() else [],
             }
             self.model_conf = (modeType, model_param, allowedList, names, rainbows)
         except Exception:
@@ -107,15 +117,14 @@ class cityManagementModel:
                                    ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
 def detSeg_demo2(args):
     model_conf, frame, request_id = args
-    modelList, postProcess = model_conf[1]['modelList'], model_conf[1]['postProcess']
+    modelList, postProcess,score_byClass,fiterList = (
+        model_conf[1]['modelList'], model_conf[1]['postProcess'],model_conf[1]['score_byClass'], model_conf[1]['fiterList'])
     try:
-        result =   [[ None, None, AI_process_N([frame], modelList, postProcess)[0] ]  ]   # 为了让返回值适配统一的接口而写的shi
+        result =   [[ None, None, AI_process_N([frame], modelList, postProcess,score_byClass,fiterList)[0] ]  ]   # 为了让返回值适配统一的接口而写的shi
         return result
     except ServiceException as s:
         raise s
     except Exception:
-        # self.num += 1
-        # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
         logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
         raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                                ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
@@ -123,11 +132,6 @@ def detSeg_demo2(args):
 def model_process(args):
     model_conf, frame, request_id = args
     model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
-    # modeType, model_param, allowedList, names, rainbows = model_conf
-    #   segmodel, names, label_arraylist, rainbows, objectPar, font, segPar, mode, postPar, requestId = args
-    # model_param['digitFont'] = digitFont
-    # model_param['label_arraylist'] = label_arraylist
-    # model_param['font_config'] = font_config
     try:
         return AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
                           rainbows, objectPar=model_param['objectPar'], font=model_param['digitFont'],
@@ -160,7 +164,13 @@ class TwoModel:
             Detweights = par['Detweights']
             with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
                 model = runtime.deserialize_cuda_engine(f.read())
-            segmodel = None
+            if modeType == ModelType.CITY_FIREAREA_MODEL:
+                sam = sam_model_registry[par['sam_type']](checkpoint=par['Samweights'])
+                sam.to(device=device)
+                segmodel = SamPredictor(sam)
+            else:
+                segmodel = None
+
             postFile = par['postFile']
             conf_thres = postFile["conf_thres"]
             iou_thres = postFile["iou_thres"]
@@ -174,7 +184,10 @@ class TwoModel:
                 "conf_thres": conf_thres,
                 "iou_thres": iou_thres,
                 "trtFlag_det": par['trtFlag_det'],
-                "otc": otc
+                "otc": otc,
+                "ksize":par['ksize'] if 'ksize' in par.keys() else None,
+                "score_byClass": par['score_byClass'] if 'score_byClass' in par.keys() else None,
+                "fiterList": par['fiterList'] if 'fiterList' in par.keys() else []
             }
             self.model_conf = (modeType, model_param, allowedList, names, rainbows)
         except Exception:
@@ -182,16 +195,15 @@ class TwoModel:
             raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                    ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
         logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
-
-
 def forest_process(args):
     model_conf, frame, request_id = args
     model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
     try:
         return AI_process_forest([frame], model_param['model'], model_param['segmodel'], names,
                                  model_param['label_arraylist'], rainbows, model_param['half'], model_param['device'],
-                                 model_param['conf_thres'], model_param['iou_thres'], [], font=model_param['digitFont'],
-                                 trtFlag_det=model_param['trtFlag_det'], SecNms=model_param['otc'])
+                                 model_param['conf_thres'], model_param['iou_thres'],font=model_param['digitFont'],
+                                 trtFlag_det=model_param['trtFlag_det'], SecNms=model_param['otc'],ksize = model_param['ksize'],
+                                 score_byClass=model_param['score_byClass'],fiterList=model_param['fiterList'])
     except ServiceException as s:
         raise s
     except Exception:
@@ -200,7 +212,6 @@ def forest_process(args):
         logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
         raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                                ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
 class MultiModel:
     __slots__ = "model_conf"
 
@@ -219,6 +230,8 @@ class MultiModel:
             model_param = {
                 "modelList": modelList,
                 "postProcess": postProcess,
+                "score_byClass": par['score_byClass'] if 'score_byClass' in par.keys() else None,
+                "fiterList": par['fiterList'] if 'fiterList' in par.keys() else []
             }
             self.model_conf = (modeType, model_param, allowedList, names, rainbows)
         except Exception:
@@ -226,13 +239,13 @@ class MultiModel:
             raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                    ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
         logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
-
 def channel2_process(args):
     model_conf, frame, request_id = args
-    modelList, postProcess = model_conf[1]['modelList'], model_conf[1]['postProcess']
+    modelList, postProcess,score_byClass,fiterList = (
+        model_conf[1]['modelList'], model_conf[1]['postProcess'],model_conf[1]['score_byClass'], model_conf[1]['fiterList'])
     try:
         start = time.time()
-        result = [[None, None, AI_process_C([frame], modelList, postProcess)[0]]]   # 为了让返回值适配统一的接口而写的shi
+        result = [[None, None, AI_process_C([frame], modelList, postProcess,score_byClass,fiterList)[0]]]   # 为了让返回值适配统一的接口而写的shi
         # print("AI_process_C use time = {}".format(time.time()-start))
         return result
     except ServiceException as s:
@@ -241,7 +254,6 @@ def channel2_process(args):
         logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
         raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                                ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
 def get_label_arraylist(*args):
     width, height, names, rainbows = args
     # line = int(round(0.002 * (height + width) / 2) + 1)
@@ -262,8 +274,6 @@ def get_label_arraylist(*args):
                  'label_location': 'leftTop'}
     label_arraylist = get_label_arrays(names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
     return digitFont, label_arraylist, (line, text_width, text_height, fontScale, tf)
-
-
 # 船只模型
 class ShipModel:
     __slots__ = "model_conf"
@@ -289,8 +299,6 @@ class ShipModel:
             raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                    ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
         logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
-
-
 def obb_process(args):
     model_conf, frame, request_id = args
     model_param = model_conf[1]
@@ -305,7 +313,6 @@ def obb_process(args):
         logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
         raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                                ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
 # 车牌分割模型、健康码、行程码分割模型
 class IMModel:
     __slots__ = "model_conf"
@@ -329,7 +336,7 @@ class IMModel:
 
             new_device = torch.device(par['device'])
             model = torch.jit.load(par[img_type]['weights'])
-            logger.info("########################加载 jit 模型成功 成功 ########################, requestId:{}", 
+            logger.info("########################加载 jit 模型成功 成功 ########################, requestId:{}",
                         requestId)
             self.model_conf = (modeType, allowedList, new_device, model, par, img_type)
         except Exception:
@@ -383,15 +390,12 @@ class CARPLATEModel:
             detpar = par['models'][0]['par']
             # new_device = torch.device(par['device'])
             # modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par'])  for  modelPar in par['models'] ]
-            logger.info("########################加载 plate_yolov5s_v3.jit 成功 ########################, requestId:{}",
-                        requestId)
             self.model_conf = (modeType, device, modelList, detpar, par['rainbows'])
         except Exception:
             logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
             raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                    ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
 
-
 class DENSECROWDCOUNTModel:
     __slots__ = "model_conf"
 
@@ -402,8 +406,8 @@ class DENSECROWDCOUNTModel:
             par = modeType.value[4](str(device), gpu_name)
             rainbows = par["rainbows"]
             models=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par'])  for  modelPar in par['models'] ]
-            postPar = par['models'][0]['par']
-            self.model_conf = (modeType, device, models[0], postPar, rainbows)
+            postPar = [pp['par'] for pp in par['models']]
+            self.model_conf = (modeType, device, models, postPar, rainbows)
         except Exception:
             logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
             raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
@@ -752,4 +756,25 @@ MODEL_CONFIG = {
         None,
         lambda x: cc_process(x)
     ),
+    # 加载建筑物下行人检测模型
+    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1]: (
+        lambda x, y, r, t, z, h: DENSECROWDCOUNTModel(x, y, r, ModelType.CITY_UNDERBUILDCOUNT_MODEL, t, z, h),
+        ModelType.CITY_UNDERBUILDCOUNT_MODEL,
+        None,
+        lambda x: cc_process(x)
+    ),
+    # 加载火焰面积模型
+    ModelType.CITY_FIREAREA_MODEL.value[1]: (
+        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CITY_FIREAREA_MODEL, t, z, h),
+        ModelType.CITY_FIREAREA_MODEL,
+        lambda x, y, z: one_label(x, y, z),
+        lambda x: forest_process(x)
+    ),
+    # 加载安防模型
+    ModelType.CITY_SECURITY_MODEL.value[1]: (
+        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_SECURITY_MODEL, t, z, h),
+        ModelType.CITY_SECURITY_MODEL,
+        lambda x, y, z: one_label(x, y, z),
+        lambda x: detSeg_demo2(x)
+    ),   
 }

util/PlotsUtils.py

@@ -2,9 +2,10 @@ import cv2
 import numpy as np
 from PIL import Image, ImageDraw, ImageFont
 import unicodedata
+from loguru import logger
 FONT_PATH = "../AIlib2/conf/platech.ttf"
 
-zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8") 
+zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8")
 
 def get_label_array(color=None, label=None, font=None, fontSize=40, unify=False):
     if unify:
@@ -23,7 +24,6 @@ def get_label_array(color=None, label=None, font=None, fontSize=40, unify=False)
     im_array = cv2.resize(im_array, (0, 0), fx=scale, fy=scale)
     return im_array
 
-
 def get_label_arrays(labelNames, colors, fontSize=40, fontPath="platech.ttf"):
     font = ImageFont.truetype(fontPath, fontSize, encoding='utf-8')
     label_arraylist = [get_label_array(colors[i % 20], label_name, font, fontSize) for i, label_name in
@@ -49,6 +49,48 @@ def get_label_array_dict(colors, fontSize=40, fontPath="platech.ttf"):
         zh_dict[code] = arr
     return zh_dict
 
+def get_label_left(x0,y1,label_array,img):
+    imh, imw = img.shape[0:2]
+    lh, lw = label_array.shape[0:2]
+    # x1 框框左上x位置 + 描述的宽
+    # y0 框框左上y位置 - 描述的高
+    x1, y0 = x0 + lw, y1 - lh
+    # 如果y0小于0, 说明超过上边框
+    if y0 < 0:
+        y0 = 0
+        # y1等于文字高度
+        y1 = y0 + lh
+    # 如果y1框框的高大于图片高度
+    if y1 > imh:
+        # y1等于图片高度
+        y1 = imh
+        # y0等于y1减去文字高度
+        y0 = y1 - lh
+    # 如果x0小于0
+    if x0 < 0:
+        x0 = 0
+        x1 = x0 + lw
+    if x1 > imw:
+        x1 = imw
+        x0 = x1 - lw
+    return x0,y0,x1,y1
+
+def get_label_right(x1,y0,label_array):
+    lh, lw = label_array.shape[0:2]
+    # x1 框框右上x位置 + 描述的宽
+    # y0 框框右上y位置 - 描述的高
+    x0, y1 = x1 - lw, y0 - lh
+    # 如果y0小于0, 说明超过上边框
+    if y0 < 0 or y1 < 0:
+        y1 = 0
+        # y1等于文字高度
+        y0 = y1 + lh
+    # 如果x0小于0
+    if x0 < 0 or x1 < 0:
+        x0 = 0
+        x1 = x0 + lw
+
+    return x0,y1,x1,y0
 
 def xywh2xyxy(box):
     if not isinstance(box[0], (list, tuple, np.ndarray)):
@@ -74,42 +116,24 @@ def xy2xyxy(box):
         box = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
     return box
 
-def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=None, isNew=False):
+def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=None, isNew=False, border=None):
     # 识别问题描述图片的高、宽
-    lh, lw = label_array.shape[0:2]
     # 图片的长度和宽度
-    imh, imw = img.shape[0:2]
+    if border is not None:
+        border = np.array(border,np.int32)
+        color,label_array=draw_name_border(box,color,label_array,border)
+        #img = draw_transparent_red_polygon(img,border,'',alpha=0.1)
+
+    lh, lw = label_array.shape[0:2]
+    tl = config[0]
+    if isinstance(box[-1], np.ndarray):
+        return draw_name_points(img,box,color)
+
+    label = ' %.2f' % score
     box = xywh2xyxy(box)
     # 框框左上的位置
     x0, y1 = box[0][0], box[0][1]
-    # if score_location == 'leftTop':
-    #     x0, y1 = box[0][0], box[0][1]
-    # # 框框左下的位置
-    # elif score_location == 'leftBottom':
-    #     x0, y1 = box[3][0], box[3][1]
-    # else:
-    #     x0, y1 = box[0][0], box[0][1]
-    # x1 框框左上x位置 + 描述的宽
-    # y0 框框左上y位置 - 描述的高
-    x1, y0 = x0 + lw, y1 - lh
-    # 如果y0小于0, 说明超过上边框
-    if y0 < 0:
-        y0 = 0
-        # y1等于文字高度
-        y1 = y0 + lh
-    # 如果y1框框的高大于图片高度
-    if y1 > imh:
-        # y1等于图片高度
-        y1 = imh
-        # y0等于y1减去文字高度
-        y0 = y1 - lh
-    # 如果x0小于0
-    if x0 < 0:
-        x0 = 0
-        x1 = x0 + lw
-    if x1 > imw:
-        x1 = imw
-        x0 = x1 - lw
+    x0, y0, x1, y1 = get_label_left(x0, y1, label_array, img)
     # box_tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
     '''
     1. img（array） 为ndarray类型（可以为cv.imread）直接读取的数据
@@ -119,14 +143,12 @@ def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=Non
     5. thickness（int）：画线的粗细
     6. shift：顶点坐标中小数的位数
     '''
-    tl = config[0]
+    img[y0:y1, x0:x1, :] = label_array
     box1 = np.asarray(box, np.int32)
     cv2.polylines(img, [box1], True, color, tl)
-    img[y0:y1, x0:x1, :] = label_array
     pts_cls = [(x0, y0), (x1, y1)]
     # 把英文字符score画到类别旁边
     # tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
-    label = ' %.2f' % score
     # tf = max(tl, 1)
     # fontScale = float(format(imw / 1920 * 1.1, '.2f')) or tl * 0.33
     # fontScale = tl * 0.33
@@ -224,12 +246,11 @@ def draw_name_joint(box, img, label_array_dict, score=0.5, color=None, config=No
     cv2.putText(img, label, p3, 0, config[3], [225, 255, 255], thickness=config[4], lineType=cv2.LINE_AA)
     return img, box
 
-def draw_name_ocr(box, img, color, label, line_thickness=2, outfontsize=40):
+def draw_name_ocr(box, img, color, line_thickness=2, outfontsize=40):
     font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
-    #(color=None, label=None, font=None, fontSize=40, unify=False)
-    label_zh = get_label_array(color, label, font, outfontsize)
-    return plot_one_box_auto(box, img, color, line_thickness, label_zh)
-
+    # (color=None, label=None, font=None, fontSize=40, unify=False)
+    label_zh = get_label_array(color, box[0], font, outfontsize)
+    return plot_one_box_auto(box[1], img, color, line_thickness, label_zh)
 def filterBox(det0, det1, pix_dis):
     # det0为 (m1, 11) 矩阵
     # det1为 (m2, 12) 矩阵
@@ -262,7 +283,7 @@ def filterBox(det0, det1, pix_dis):
     x_c, y_c = (x3+x4)//2, (y3+y4)//2
     dis = (x2_c - x_c)**2 + (y2_c - y_c)**2
     mask = (joint_det[..., 9] == joint_det[..., 20]) & (dis <= pix_dis**2)
-    
+
     # 类别相同 & 中心点在上一帧的框内 判断为True
     res = np.sum(mask, axis=1)
     det0_copy[..., -1] = res
@@ -275,6 +296,7 @@ def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
     # print("省略 :%s, lh:%s, lw:%s"%('+++' * 10, lh, lw))
     # 图片的长度和宽度
     imh, imw = img.shape[0:2]
+    points = None
     box = xy2xyxy(box)
     # 框框左上的位置
     x0, y1 = box[0][0], box[0][1]
@@ -315,22 +337,140 @@ def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
 
     return img, box
 
-
-def draw_name_crowd(dets, img, color, label, line_thickness=2, outfontsize=20):
+def draw_name_crowd(dets, img, color, outfontsize=20):
     font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
-    H,W = img.shape[:2]
-    # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    # img = Image.fromarray(img)
-    # width, height = img.size
-    Wrate = W // 128 * 128/W
-    Hrate = H // 128 * 128/H
+    if len(dets) == 2:
+        label = '当前人数：%d'%len(dets[0])
+        detP = dets[0]
+        line = dets[1]
+        for p in detP:
+            img = cv2.circle(img, (int(p[0]), int(p[1])), line, color, -1)
+        label_arr = get_label_array(color, label, font, outfontsize)
+        lh, lw = label_arr.shape[0:2]
+        img[0:lh, 0:lw, :] = label_arr
+    elif len(dets) == 3:
+        detP = dets[1]
+        line = dets[2]
+        for p in detP:
+            img = cv2.circle(img, (int(p[0]), int(p[1])), line, color, -1)
 
-    # img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+        detM = dets[0]
+        h, w = img.shape[:2]
+        for b in detM:
+            label = '该建筑下行人及数量：%d'%(int(b[4]))
+            label_arr = get_label_array(color, label, font, outfontsize)
+            lh, lw = label_arr.shape[0:2]
+            # 框框左上的位置
+            x0, y1 = int(b[0]), int(b[1])
+            # print("省略 :%s, x0:%s, y1:%s"%('+++' * 10, x0, y1))
+            x1, y0 = x0 + lw, y1 - lh
+            # 如果y0小于0, 说明超过上边框
+            if y0 < 0:
+                y0 = 0
+                # y1等于文字高度
+                y1 = y0 + lh
+            # 如果y1框框的高大于图片高度
+            if y1 > h:
+                # y1等于图片高度
+                y1 = h
+                # y0等于y1减去文字高度
+                y0 = y1 - lh
+            # 如果x0小于0
+            if x0 < 0:
+                x0 = 0
+                x1 = x0 + lw
+            if x1 > w:
+                x1 = w
+                x0 = x1 - lw
 
-    for p in dets:
-        img = cv2.circle(img, (int(p[0]/Wrate), int(p[1]/Hrate)), line_thickness, color, -1)
-    Calc_label_arr = get_label_array(color, label, font, outfontsize)
-    lh, lw = Calc_label_arr.shape[0:2]
-    img[0:lh, 0:lw, :] = Calc_label_arr
+            cv2.polylines(img, [np.asarray(xy2xyxy(b), np.int32)], True, (0, 128, 255), 2)
+            img[y0:y1, x0:x1, :] = label_arr
 
-    return img, dets
+
+    return img, dets
+
+def draw_name_points(img,box,color):
+    font = ImageFont.truetype(FONT_PATH, 6, encoding='utf-8')
+    points = box[-1]
+    arrea = cv2.contourArea(points)
+    label = '火焰'
+    arealabel = '面积:%s' % f"{arrea:.1e}"
+    label_array_area = get_label_array(color, arealabel, font, 10)
+    label_array = get_label_array(color, label, font, 10)
+    lh_area, lw_area = label_array_area.shape[0:2]
+    box = box[:4]
+    # 框框左上的位置
+    x0, y1 = box[0][0], max(box[0][1] - lh_area - 3, 0)
+    x1, y0 = box[1][0], box[1][1]
+    x0_label, y0_label, x1_label, y1_label = get_label_left(x0, y1, label_array, img)
+    x0_area, y0_area, x1_area, y1_area = get_label_right(x1, y0, label_array_area)
+    img[y0_label:y1_label, x0_label:x1_label, :] = label_array
+    img[y0_area:y1_area, x0_area:x1_area, :] = label_array_area
+    # cv2.drawContours(img, points, -1, color, tl)
+    cv2.polylines(img, [points], False, color, 2)
+    if lw_area < box[1][0] - box[0][0]:
+        box = [(x0, y1), (x1, y1), (x1, box[2][1]), (x0, box[2][1])]
+    else:
+        box = [(x0_label, y1), (x1, y1), (x1, box[2][1]), (x0_label, box[2][1])]
+    box = np.asarray(box, np.int32)
+    cv2.polylines(img, [box], True, color, 2)
+    return img, box
+
+def draw_name_border(box,color,label_array,border):
+    box = xywh2xyxy(box[:4])
+    cx, cy = int((box[0][0] + box[2][0]) / 2), int((box[0][1] + box[2][1]) / 2)
+    flag = cv2.pointPolygonTest(border, (int(cx), int(cy)),
+                                False)  # 若为False，会找点是否在内，外，或轮廓上
+    if flag == 1:
+        color = [0, 0, 255]
+        # 纯白色是(255, 255, 255)，根据容差定义白色范围
+        lower_white = np.array([255 - 30] * 3, dtype=np.uint8)
+        upper_white = np.array([255, 255, 255], dtype=np.uint8)
+        # 创建白色区域的掩码（白色区域为True，非白色为False）
+        white_mask = cv2.inRange(label_array, lower_white, upper_white)
+        # 创建与原图相同大小的目标颜色图像
+        target_img = np.full_like(label_array, color, dtype=np.uint8)
+        # 先将非白色区域设为目标颜色，再将白色区域覆盖回原图颜色
+        label_array = np.where(white_mask[..., None], label_array, target_img)
+    return color,label_array
+
+def draw_transparent_red_polygon(img, points, alpha=0.5):
+    """
+    在图像中指定的多边形区域绘制半透明红色
+
+    参数:
+        image_path: 原始图像路径
+        points: 多边形顶点坐标列表，格式为[(x1,y1), (x2,y2), ..., (xn,yn)]
+        output_path: 输出图像路径
+        alpha: 透明度系数，0-1之间，值越小透明度越高
+    """
+    # 读取原始图像
+    if img is None:
+        raise ValueError(f"无法读取图像")
+
+    # 创建与原图大小相同的透明图层（RGBA格式）
+    overlay = np.zeros((img.shape[0], img.shape[1], 4), dtype=np.uint8)
+
+    # 将点列表转换为适合cv2.fillPoly的格式
+    #pts = np.array(points, np.int32)
+    pts = points.reshape((-1, 1, 2))
+
+    # 在透明图层上绘制红色多边形（BGR为0,0,255）
+    # 最后一个通道是Alpha值，控制透明度，黄色rgb
+    cv2.fillPoly(overlay, [pts], (255, 0, 0, int(alpha * 255)))
+
+    # 将透明图层转换为BGR格式（用于与原图混合）
+    overlay_bgr = cv2.cvtColor(overlay, cv2.COLOR_RGBA2BGR)
+
+    # 创建掩码，用于提取红色区域
+    mask = overlay[:, :, 3] / 255.0
+    mask = np.stack([mask] * 3, axis=-1)  # 转换为3通道
+
+    # 混合原图和透明红色区域
+    img = img * (1 - mask) + overlay_bgr * mask
+    img = img.astype(np.uint8)
+
+    # # 保存结果
+    # cv2.imwrite(output_path, result)
+
+    return img