Merge pull request 'zsl' (#8) from zsl into master

Reviewed-on: gitadmin/tuoheng_algN#8

README.md

@@ -0,0 +1,8 @@
+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 

concurrency/FileUploadThread.py

@@ -19,22 +19,21 @@ 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', '_mqtt_list')
 
     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, self._mqtt_list = args
         self._storage_source = self._context['service']['storage_source']
-        self._algStatus = False # 默认关闭
-        
+        self._algStatus = False  # 默认关闭
+
         # self._algStatus = True # 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
@@ -44,15 +43,15 @@ class FileUpload(Thread):
             # 这里放非默认逻辑的代码
             self._algSwitch = False
 
-        print("---line46 :FileUploadThread.py---",self._algSwitch)
+        print("---line46 :FileUploadThread.py---", self._algSwitch)
 
 
-#如果任务是在线、离线处理，则用此类
+# 如果任务是在线、离线处理，则用此类
 class ImageFileUpload(FileUpload):
     __slots__ = ()
 
-    #@staticmethod
-    def handle_image(self,frame_msg, frame_step):
+    # @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)
         det_xywh, frame, current_frame, all_frames, font_config = frame_msg
@@ -65,8 +64,7 @@ class ImageFileUpload(FileUpload):
         模型编号：modeCode
         检测目标：detectTargetCode
         '''
-        print('*'*100,' mqtt_list:',len(self._mqtt_list))
-
+        print('*' * 100, ' mqtt_list:', len(self._mqtt_list))
 
         model_info = []
         # 更加模型编码解析数据
@@ -78,12 +76,10 @@ class ImageFileUpload(FileUpload):
                         for target in target_list:
                             # 自研车牌模型判断
                             if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
-                                box = [target[1][0][0], target[1][0][1], target[1][3][0], target[1][3][1]]
-                                draw_name_ocr(box, aFrame, target[4], target[0])
-                                cls = 0
-                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
-                                draw_name_crowd(target[3], aFrame, target[4], cls)
-                                cls = 0
+                                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])
@@ -115,13 +111,15 @@ 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()
@@ -137,12 +135,14 @@ class ImageFileUpload(FileUpload):
                         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)
@@ -153,8 +153,8 @@ 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)
@@ -169,38 +169,38 @@ class ImageFileUpload(FileUpload):
                                                                          model_info["modelCode"],
                                                                          model_info["detectTargetCode"],
                                                                          request_id)
-                                        if self._storage_source==1:
+                                        if self._storage_source == 1:
                                             ai_future = t.submit(minioSdk.put_object, ai_image,
-                                                             ai_image_name)
+                                                                 ai_image_name)
                                         else:
                                             ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
-                                                             ai_image.tobytes())
+                                                                 ai_image.tobytes())
 
                                         task.append(ai_future)
-                                        #msg_list.append(message_feedback(request_id,
+                                        # 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=[]
+                                    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):
-                                        msg_list.append(  message_feedback(request_id,
+                                    for ii, model_info in enumerate(model_info_list):
+                                        msg_list.append(message_feedback(request_id,
                                                                          AnalysisStatus.RUNNING.value,
                                                                          analyse_type, "", "", "",
                                                                          remote_image_list[0],
-                                                                         remote_image_list[ii+1],
+                                                                         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):
+                                    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
@@ -227,7 +227,7 @@ def build_image_name(*args):
                                                           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')
 
@@ -235,6 +235,7 @@ 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):
         """
@@ -256,9 +257,10 @@ class ImageTypeImageFileUpload(Thread):
                         for target in target_list:
                             # 自研车牌模型判断
                             if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
-                                draw_name_ocr(target[1], aiFrame, font_config[cls], target[0])
-                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
-                                draw_name_crowd(target[1],aiFrame,font_config[cls],target[0])
+                                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)
 
@@ -287,8 +289,8 @@ 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)
 
@@ -313,14 +315,14 @@ class ImageTypeImageFileUpload(Thread):
                                     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,
@@ -336,12 +338,12 @@ class ImageTypeImageFileUpload(Thread):
                                         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)
+                                                                           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)    
+                                            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())
@@ -357,14 +359,14 @@ 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)    
+                                            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,
@@ -389,12 +391,12 @@ 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))
@@ -406,7 +408,8 @@ 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:

concurrency/FileUploadThread_20250106.py

@@ -1,305 +0,0 @@
-# -*- coding: utf-8 -*-
-from concurrent.futures import ThreadPoolExecutor
-from threading import Thread
-from time import sleep, time
-from traceback import format_exc
-
-from loguru import logger
-import cv2
-
-from entity.FeedBack import message_feedback
-from enums.ExceptionEnum import ExceptionType
-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.QueUtil import put_queue, get_no_block_queue, clear_queue
-import io
-
-class FileUpload(Thread):
-    __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 = args
-        self._storage_source = self._context['service']['storage_source']
-
-class ImageFileUpload(FileUpload):
-    __slots__ = ()
-
-    @staticmethod
-    def handle_image(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)
-        det_xywh, frame, current_frame, all_frames, font_config = frame_msg
-        '''
-        det_xywh:{
-            'code':{
-                1: [[detect_targets_code, box, score, label_array, color]]
-            }
-        }
-        模型编号：modeCode
-        检测目标：detectTargetCode
-        '''
-        model_info = []
-        # 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])
-                        model_info.append({"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame})
-        if len(model_info) > 0:
-            image_result = {
-                "or_frame": frame,
-                "model_info": model_info,
-                "current_frame": current_frame,
-                "last_frame": current_frame + frame_step
-            }
-            return image_result
-        return None
-
-    def run(self):
-        msg, context = self._msg, self._context
-        service = context["service"]
-        base_dir, env, request_id = context["base_dir"], context["env"], msg["request_id"]
-        logger.info("启动图片上传线程, requestId: {}", request_id)
-        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
-        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 )
-                else:
-                    aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
-                start_time = time()
-                while True:
-                    try:
-                        if time() - start_time > service_timeout:
-                            logger.error("图片上传线程运行超时, requestId: {}", request_id)
-                            break
-                            raise ServiceException(ExceptionType.TASK_EXCUTE_TIMEOUT.value[0],
-                                                   ExceptionType.TASK_EXCUTE_TIMEOUT.value[1])
-                        # 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:
-                                if 'stop' == image_msg[1]:
-                                    logger.info("开始停止图片上传线程, requestId:{}", request_id)
-                                    break
-                            if image_msg[0] == 1:
-                                image_result = self.handle_image(image_msg[1], frame_step)
-                                if image_result is not None:
-                                    task = []
-                                    or_image = cv2.imencode(".jpg", image_result["or_frame"])[1]
-                                    or_image_name = build_image_name(image_result["current_frame"],
-                                                                     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)    
-                                    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"]
-                                    msg_list = []
-                                    for model_info in model_info_list:
-                                        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   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)
-                                        msg_list.append(message_feedback(request_id,
-                                                                         AnalysisStatus.RUNNING.value,
-                                                                         analyse_type, "", "", "",
-                                                                         or_image_name,
-                                                                         ai_image_name,
-                                                                         model_info['modelCode'],
-                                                                         model_info['detectTargetCode']))
-                                    for tk in task:
-                                        tk.result()
-                                    for msg in msg_list:
-                                        put_queue(fb_queue, msg, timeout=2, is_ex=False)
-                                    del task, msg_list
-                        else:
-                            sleep(1)
-                        del image_msg
-                    except Exception:
-                        logger.error("图片上传异常：{}, requestId:{}", format_exc(), request_id)
-        finally:
-            logger.info("停止图片上传线程0, requestId:{}", request_id)
-            clear_queue(image_queue)
-            logger.info("停止图片上传线程1, requestId:{}", request_id)
-
-
-def build_image_name(*args):
-    """
-        {requestId}/{time_now}_frame-{current_frame}-{last_frame}_type_{random_num}-{mode_type}" \
-                   "-{modeCode}-{target}_{image_type}.jpg
-    """
-    current_frame, last_frame, mode_type, image_type, modeCode, target, request_id = args
-    random_num = TimeUtils.now_date_to_str(TimeUtils.YMDHMSF)
-    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)
-
-
-class ImageTypeImageFileUpload(Thread):
-    __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 = args
-        self._storage_source = self._context['service']['storage_source']
-    @staticmethod
-    def handle_image(det_xywh, copy_frame, font_config):
-        """
-            det_xywh:{
-                'code':{
-                    1: [[detect_targets_code, box, score, label_array, color]]
-                }
-            }
-            模型编号：modeCode
-            检测目标：detectTargetCode
-        """
-        model_info = []
-        # 更加模型编码解析数据
-        for code, det_info in det_xywh.items():
-            if det_info is not None and len(det_info) > 0:
-                for cls, target_list in det_info.items():
-                    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
-                        })
-        if len(model_info) > 0:
-            image_result = {
-                "or_frame": copy_frame,
-                "model_info": model_info,
-                "current_frame": 0,
-                "last_frame": 0
-            }
-            return image_result
-        return None
-
-    def run(self):
-        context, msg = self._context, self._msg
-        base_dir, env, request_id = context["base_dir"], context["env"], msg["request_id"]
-        logger.info("启动图片识别图片上传线程, requestId: {}", request_id)
-        image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
-        service_timeout = int(context["service"]["timeout"])
-        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 )
-                else:
-                    aliyunOssSdk = AliyunOssSdk(base_dir, env, request_id)
-                
-                start_time = time()
-                while True:
-                    try:
-                        if time() - start_time > service_timeout:
-                            logger.error("图片上传进程运行超时, requestId: {}", request_id)
-                            break
-                        # 获取队列中的消息
-                        image_msg = image_queue.get()
-                        if image_msg is not None:
-                            if image_msg[0] == 2:
-                                if 'stop' == image_msg[1]:
-                                    logger.info("开始停止图片上传线程, requestId:{}", request_id)
-                                    break
-                            if image_msg[0] == 1:
-                                task, msg_list = [], []
-                                det_xywh, image_url, copy_frame, font_config, result = image_msg[1]
-                                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)    
-                                    else:
-                                        ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name, copy_frame)
-                                                                     
-                                    task.append(ai_future)
-                                    msg_list.append(message_feedback(request_id,
-                                                                     AnalysisStatus.RUNNING.value,
-                                                                     analyse_type, "", "", "",
-                                                                     image_url,
-                                                                     ai_image_name,
-                                                                     result.get("modelCode"),
-                                                                     result.get("type"),
-                                                                     analyse_results=result))
-                                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 = 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)    
-                                            else:
-                                                or_future = t.submit(aliyunOssSdk.put_object, image_url,
-                                                                     or_image.tobytes())
-                                            task.append(or_future)
-                                        model_info_list = image_result.get("model_info")
-                                        for model_info in model_info_list:
-                                            ai_result, ai_image = cv2.imencode(".jpg", model_info.get("frame"))
-                                            ai_image_name = build_image_name(image_result.get("current_frame"),
-                                                                             image_result.get("last_frame"),
-                                                                             analyse_type,
-                                                                             "AI",
-                                                                             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:    
-                                                ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
-                                                                     ai_image.tobytes())
-                                            task.append(ai_future)
-                                            msg_list.append(message_feedback(request_id,
-                                                                             AnalysisStatus.RUNNING.value,
-                                                                             analyse_type, "", "", "",
-                                                                             image_url,
-                                                                             ai_image_name,
-                                                                             model_info.get('modelCode'),
-                                                                             model_info.get('detectTargetCode'),
-                                                                             analyse_results=result))
-                                for thread_result in task:
-                                    thread_result.result()
-                                for msg in msg_list:
-                                    put_queue(fb_queue, msg, timeout=2, is_ex=False)
-                        else:
-                            sleep(1)
-                    except Exception as e:
-                        logger.error("图片上传异常：{}, requestId:{}", format_exc(), request_id)
-            finally:
-                clear_queue(image_queue)
-        logger.info("停止图片识别图片上传线程, requestId:{}", request_id)

concurrency/IntelligentRecognitionProcess.py

@@ -62,7 +62,7 @@ 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 +92,6 @@ class IntelligentRecognitionProcess(Process):
         return hb_thread
 
 
-
-
 class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
     __slots__ = ()
 
@@ -112,19 +110,16 @@ 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()
@@ -145,7 +140,7 @@ 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 +220,7 @@ 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 +235,14 @@ 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 +295,8 @@ 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]
@@ -331,11 +327,11 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                                           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 +443,12 @@ 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)
@@ -626,7 +622,8 @@ 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]
@@ -766,7 +763,7 @@ 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 +940,7 @@ 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 +979,6 @@ 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")
@@ -1271,7 +1267,8 @@ 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:
@@ -1486,9 +1483,9 @@ 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)

concurrency/PushVideoStreamProcess.py

@@ -151,35 +151,35 @@ class OnPushStreamProcess(PushStreamProcess):
                                             # 自研车牌模型处理
                                             if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
                                                 cls = 0
-                                                ocrlabel, xybox = qs
-                                                box = xy2xyxy(xybox)
+                                                box = xy2xyxy(qs[1])
                                                 score = None
                                                 color = rainbows[cls]
                                                 label_array = None
-                                                rr = t.submit(draw_name_ocr, xybox, copy_frame, color, ocrlabel)
-                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
+                                                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
+                                                # 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, points, copy_frame, color, crowdlabel)
+                                                rr = t.submit(draw_name_crowd, qs, copy_frame, color)
                                             else:
-                                                 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])
-                                                 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)
+                                                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])
+                                                else:
+                                                    rr = t.submit(draw_painting_joint, box, copy_frame, label_array,
+                                                                  score, color, font_config)
 
                                             thread_p.append(rr)
                                             if det_xywh.get(code) is None:
@@ -261,17 +261,18 @@ class OnPushStreamProcess(PushStreamProcess):
                                             is_new = False
                                             if q[11] == 1:
                                                 is_new = True
-                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
-                                                cls = ocrlabel
-                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
-                                                cls = crowdlabel
-                                                label_array = points                                            
+                                            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)
@@ -393,22 +394,21 @@ class OffPushStreamProcess(PushStreamProcess):
                                             # 自研车牌模型处理
                                             if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
                                                 cls = 0
-                                                ocrlabel, xybox = qs
-                                                box = xy2xyxy(xybox)
+                                                box = xy2xyxy(qs[1])
                                                 score = None
                                                 color = rainbows[cls]
                                                 label_array = None
                                                 label_arrays = [None]
-                                                rr = t.submit(draw_name_ocr,xybox,copy_frame,color,ocrlabel)
+                                                rr = t.submit(draw_name_ocr, qs, copy_frame, color)
 
-                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
+                                            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, points, copy_frame, color, crowdlabel)
+                                                rr = t.submit(draw_name_crowd, qs, copy_frame, color)
 
                                             else:
                                                 box, score, cls = xywh2xyxy2(qs)
@@ -500,16 +500,15 @@ class OffPushStreamProcess(PushStreamProcess):
                                             if q[11] == 1:
                                                 is_new = True
 
-                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
-                                                cls = ocrlabel
-                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
-                                                cls = crowdlabel
-                                                label_array = points
-
+                                            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"]]))
                                 push_p = push_stream_result.result(timeout=60)

config/logger/dsp_prod_logger.yml

@@ -5,6 +5,6 @@ log_name: "dsp.log"
 log_fmt: "{time:YYYY-MM-DD HH:mm:ss.SSS} [{level}][{process.name}-{process.id}-{thread.name}-{thread.id}][{line}] {module}-{function} - {message}"
 level: "INFO"
 rotation: "00:00"
-retention: "7 days"
+retention: "15 days"
 encoding: "utf8"
 

config/logger/dsp_test_logger.yml

@@ -5,6 +5,6 @@ log_name: "dsp.log"
 log_fmt: "{time:YYYY-MM-DD HH:mm:ss.SSS} [{level}][{process.name}-{process.id}-{thread.name}-{thread.id}][{line}] {module}-{function} - {message}"
 level: "INFO"
 rotation: "00:00"
-retention: "3 days"
+retention: "7 days"
 encoding: "utf8"
 

enums/ModelTypeEnum-jcq.py

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+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

@@ -374,8 +374,8 @@ 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  } }
             },
@@ -633,8 +633,6 @@ 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}},
@@ -976,12 +974,14 @@ 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,
                 },
             }],
     })
@@ -1006,6 +1006,42 @@ 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,
+                        '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}},
+            },
+            {
+                '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
+                },
+            }],
+    })
+
     @staticmethod
     def checkCode(code):
         for model in ModelType:

readme.md

@@ -1,8 +0,0 @@
-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。

service/Dispatcher-raw.py

@@ -1,507 +0,0 @@
-# -*- coding: utf-8 -*-
-import time,os
-from os.path import join
-from traceback import format_exc
-import json
-from cerberus import Validator
-
-from common.Constant import ONLINE_START_SCHEMA, ONLINE_STOP_SCHEMA, OFFLINE_START_SCHEMA, OFFLINE_STOP_SCHEMA, \
-    IMAGE_SCHEMA, RECORDING_START_SCHEMA, RECORDING_STOP_SCHEMA, PULL2PUSH_START_SCHEMA, PULL2PUSH_STOP_SCHEMA
-from common.YmlConstant import service_yml_path, kafka_yml_path
-from concurrency.FeedbackThread import FeedbackThread
-from concurrency.uploadGPU import uploadGPUinfos
-from concurrency.IntelligentRecognitionProcess2 import OnlineIntelligentRecognitionProcess2, \
-    OfflineIntelligentRecognitionProcess2, PhotosIntelligentRecognitionProcess2
-from concurrency.Pull2PushStreamProcess import PushStreamProcess
-from entity.FeedBack import message_feedback, recording_feedback, pull_stream_feedback
-from enums.AnalysisStatusEnum import AnalysisStatus
-from enums.AnalysisTypeEnum import AnalysisType
-from enums.ExceptionEnum import ExceptionType
-from enums.ModelTypeEnum import ModelMethodTypeEnum, ModelType
-from enums.RecordingStatusEnum import RecordingStatus
-from enums.StatusEnum import PushStreamStatus, ExecuteStatus
-from exception.CustomerException import ServiceException
-from loguru import logger
-from multiprocessing import Queue
-from concurrency.IntelligentRecognitionProcess import OnlineIntelligentRecognitionProcess, \
-    OfflineIntelligentRecognitionProcess, PhotosIntelligentRecognitionProcess, ScreenRecordingProcess
-from util.CpuUtils import print_cpu_ex_status
-from util.FileUtils import create_dir_not_exist
-from util.GPUtils import get_first_gpu_name, print_gpu_ex_status, check_cude_is_available,select_best_server
-from util.KafkaUtils import CustomerKafkaConsumer
-from util.QueUtil import put_queue
-from util.RWUtils import getConfigs
-from kafka import KafkaProducer, KafkaConsumer
-'''
-   分发服务
-'''
-
-
-class DispatcherService:
-    __slots__ = ('__context', '__feedbackThread', '__listeningProcesses', '__fbQueue', '__topics','__taskType', '__task_type',
-                 '__kafka_config', '__recordingProcesses', '__pull2PushProcesses','__topicsPort','__gpuTopic','__role','__uploadGPUThread','__gpuDics','__producer')
-
-    def __init__(self, base_dir, env):
-        # 检测cuda是否活动
-        check_cude_is_available()
-        # 获取全局上下文配置
-        self.__context = getConfigs(join(base_dir, service_yml_path % env))
-        # 创建任务执行, 视频保存路径
-        create_dir_not_exist(join(base_dir, self.__context["video"]["file_path"]))
-        # 将根路径和环境设置到上下文中
-        self.__context["base_dir"], self.__context["env"] = base_dir, env
-
-        # 问题反馈线程
-        self.__feedbackThread,self.__uploadGPUThread, self.__fbQueue = None,None, Queue()
-        # 实时、离线、图片任务进程字典
-        self.__listeningProcesses = {}
-        # 录屏任务进程字典
-        self.__recordingProcesses = {}
-        # 转推流任务进程字典
-        self.__pull2PushProcesses = {}
-        self.__kafka_config = getConfigs(join(base_dir, kafka_yml_path % env))
-        
-        self.__producer = KafkaProducer(
-            bootstrap_servers=self.__kafka_config['bootstrap_servers'],#tencent yun
-            value_serializer=lambda v: v.encode('utf-8'))
-
-        self.__gpuDics = { }#用于存储gpu信息的字典
-        self.__role = self.__context["role"]
-        self.__topics = [
-                self.__kafka_config["topic"]["dsp-alg-online-tasks-topic"],  # 实时监听topic
-                self.__kafka_config["topic"]["dsp-alg-offline-tasks-topic"],  # 离线监听topic
-                self.__kafka_config["topic"]["dsp-alg-image-tasks-topic"],  # 图片监听topic
-                self.__kafka_config["topic"]["dsp-recording-task-topic"],  # 录屏监听topic
-                self.__kafka_config["topic"]["dsp-push-stream-task-topic"]  # 推流监听topic
-            ]
-            
-        self.__topicsPort = [
-                self.__kafka_config["topicPort"]["dsp-alg-online-tasks-topic"],  # 实时监听topic
-                self.__kafka_config["topicPort"]["dsp-alg-offline-tasks-topic"],  # 离线监听topic
-                self.__kafka_config["topicPort"]["dsp-alg-image-tasks-topic"],  # 图片监听topic
-                self.__kafka_config["topicPort"]["dsp-recording-task-topic"],  # 录屏监听topic
-                self.__kafka_config["topicPort"]["dsp-push-stream-task-topic"]  # 推流监听topic
-            ]  
-        self.__gpuTopic = [self.__kafka_config["topicGPU"]]
-        
-        if self.__role==1:
-            self.__topics = self.__topics +  self.__topicsPort + self.__gpuTopic
-   
-            
-        # 对应topic的各个lambda表达式
-        self.__task_type = {
-            self.__topics[0]: (AnalysisType.ONLINE.value, lambda x, y: self.online(x, y),
-                               lambda x, y, z: self.identify_method(x, y, z)),
-            self.__topics[1]: (AnalysisType.OFFLINE.value, lambda x, y: self.offline(x, y),
-                               lambda x, y, z: self.identify_method(x, y, z)),
-            self.__topics[2]: (AnalysisType.IMAGE.value, lambda x, y: self.image(x, y),
-                               lambda x, y, z: self.identify_method(x, y, z)),
-            self.__topics[3]: (AnalysisType.RECORDING.value, lambda x, y: self.recording(x, y),
-                               lambda x, y, z: self.recording_method(x, y, z)),
-            self.__topics[4]: (AnalysisType.PULLTOPUSH.value, lambda x, y: self.pullStream(x, y),
-                               lambda x, y, z: self.push_stream_method(x, y, z))
-                               
-        }
-        self.__taskType={
-            self.__kafka_config["topic"]["dsp-alg-online-tasks-topic"]:0,  # 实时监听topic
-            self.__kafka_config["topic"]["dsp-alg-offline-tasks-topic"]:1,  # 离线监听topic
-            self.__kafka_config["topic"]["dsp-alg-image-tasks-topic"]:2,  # 图片监听topic
-            self.__kafka_config["topic"]["dsp-recording-task-topic"]:3,  # 录屏监听topic
-            self.__kafka_config["topic"]["dsp-push-stream-task-topic"]:4  # 推流监听topic
-        }  
-        gpu_name_array = get_first_gpu_name()
-        gpu_array = [g for g in ('3090', '2080', '4090', 'A10') if g in gpu_name_array]
-        gpu_name = '2080Ti'
-        if len(gpu_array) > 0:
-            if gpu_array[0] != '2080':
-                gpu_name = gpu_array[0]
-        else:
-            raise Exception("GPU资源不在提供的模型所支持的范围内！请先提供对应的GPU模型！")
-        logger.info("当前服务环境为: {}, 服务器GPU使用型号: {}", env, gpu_name)
-        self.__context["gpu_name"] = gpu_name
-        self.start_service()
-
-    # 服务调用启动方法
-    def start_service(self):
-        # 初始化kafka监听者
-        customerKafkaConsumer = CustomerKafkaConsumer(self.__kafka_config, topics=self.__topics)
-        ####增加一个线程，用于试试监控和发送gpu状态####
-        ####
-        logger.info("(♥◠‿◠)ﾉﾞ  DSP【算法调度服务】启动成功   服务器IP:{}".format(self.__kafka_config['bootstrap_servers']  ))
-        while True:
-            try:
-                # 检查任务进程运行情况，去除结束的任务
-                self.check_process_task()
-                # 启动反馈线程
-                self.start_feedback_thread()
-                self.start_uploadGPU_thread()
-                msg = customerKafkaConsumer.poll()
-                if msg is not None and len(msg) > 0:
-                    for k, v in msg.items():
-                        for m in v:
-                            message = m.value
-                            #如果收到的信息是gpu状态的话，收到信息后，更新自己的gpu服务器状态，下面不再执行
-                            if m.topic in self.__gpuTopic:
-                                customerKafkaConsumer.commit_offset(m,'x'*16,False)
-                                #更新机器资源现状
-                                ip = message['System']['Local IP Address']
-                                self.__gpuDics[ip]=message 
-                                continue
-                            #如果收到的信息是门户消息，收到信息后，要根据Gpu状态，转发到对应的机器。
-                            elif m.topic in self.__topicsPort:
-                                customerKafkaConsumer.commit_offset(m, 'y'*16)
-                                #状态分析
-                                #recondGpu={'hostname':'thsw2','IP':'192.168.10.66','gpuId':0}
-                                recondGpu= select_best_server(self.__gpuDics)
-                                if recondGpu is None:
-                                    print(  'recondGpu：',recondGpu, '  self.__gpuDics: ',self.__gpuDics,' topic:',m.topic, ' message:',message )
-                                    continue
-                                #转发消息
-                                message['transmit_topic'] = m.topic + '-' + recondGpu['IP']
-                                transmitMsg={'transmit':message}
-                                msg_json = json.dumps( message )
-                                future =  self.__producer.send( message['transmit_topic']  ,msg_json)
-                                try:
-                                     future.get(timeout=2)
-                                     logger.info( "转发消息成功，消息topic:{},消息内容:{}",message['transmit_topic'],message   )
-                                except kafka_errors as e:
-                                     print('------transmitted error:',e)
-                                     logger.info("转发消息失败")
-                                     traceback.format_exc()
-                            else:
-                                requestId = message.get("request_id")
-                                if requestId is None:
-                                    logger.error("请求参数格式错误, 请检查请求体格式是否正确！message:%s"%(message))
-                                    continue
-                                customerKafkaConsumer.commit_offset(m, requestId)
-                                logger.info("当前拉取到的消息, topic:{}, offset:{}, partition: {}, body: {}, requestId:{}",
-                                            m.topic, m.offset, m.partition, message, requestId)
-      
-                                message['taskType']=self.__taskType[m.topic]               
-                                topic_method = self.__task_type[m.topic]
-                                topic_method[2](topic_method[1], message, topic_method[0])
-                else:
-                    print_gpu_ex_status()
-                    print_cpu_ex_status(self.__context["base_dir"])
-                    time.sleep(1)
-            except Exception:
-                logger.error("主线程异常：{}", format_exc())
-
-    def identify_method(self, handle_method, message, analysisType):
-        try:
-            check_cude_is_available()
-            handle_method(message, analysisType)
-        except ServiceException as s:
-            logger.error("消息监听异常：{}, requestId: {}", s.msg, message["request_id"])
-            put_queue(self.__fbQueue, message_feedback(message["request_id"], AnalysisStatus.FAILED.value, analysisType,
-                                                       s.code, s.msg), timeout=1)
-        except Exception:
-            logger.error("消息监听异常：{}, requestId: {}", format_exc(), message["request_id"])
-            put_queue(self.__fbQueue, message_feedback(message["request_id"], AnalysisStatus.FAILED.value, analysisType,
-                                                       ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
-                                                       ExceptionType.SERVICE_INNER_EXCEPTION.value[1]), timeout=1)
-        finally:
-            del message
-
-    def push_stream_method(self, handle_method, message, analysisType):
-        try:
-            check_cude_is_available()
-            handle_method(message, analysisType)
-        except ServiceException as s:
-            logger.error("消息监听异常：{}, requestId: {}", s.msg, message['request_id'])
-            videoInfo = [{"id": url.get("id"), "status": PushStreamStatus.FAILED.value[0]} for url in
-                         message.get("video_urls", []) if url.get("id") is not None]
-            put_queue(self.__fbQueue, pull_stream_feedback(message['request_id'], ExecuteStatus.FAILED.value[0],
-                                                           s.code, s.msg, videoInfo), timeout=1)
-        except Exception:
-            logger.error("消息监听异常：{}, requestId: {}", format_exc(), message['request_id'])
-            videoInfo = [{"id": url.get("id"), "status": PushStreamStatus.FAILED.value[0]} for url in
-                         message.get("video_urls", []) if url.get("id") is not None]
-            put_queue(self.__fbQueue, pull_stream_feedback(message.get("request_id"), ExecuteStatus.FAILED.value[0],
-                                                           ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
-                                                           ExceptionType.SERVICE_INNER_EXCEPTION.value[1], videoInfo),
-                      timeout=1)
-        finally:
-            del message
-
-    def recording_method(self, handle_method, message, analysisType):
-        try:
-            check_cude_is_available()
-            handle_method(message, analysisType)
-        except ServiceException as s:
-            logger.error("消息监听异常：{}, requestId: {}", s.msg, message["request_id"])
-            put_queue(self.__fbQueue,
-                      recording_feedback(message["request_id"], RecordingStatus.RECORDING_FAILED.value[0],
-                                         error_code=s.code, error_msg=s.msg), timeout=1)
-        except Exception:
-            logger.error("消息监听异常：{}, requestId: {}", format_exc(), message["request_id"])
-            put_queue(self.__fbQueue,
-                      recording_feedback(message["request_id"], RecordingStatus.RECORDING_FAILED.value[0],
-                                         ExceptionType.SERVICE_INNER_EXCEPTION.value[0],
-                                         ExceptionType.SERVICE_INNER_EXCEPTION.value[1]), timeout=1)
-        finally:
-            del message
-
-    # 开启实时进程
-    def startOnlineProcess(self, msg, analysisType):
-        if self.__listeningProcesses.get(msg["request_id"]):
-            logger.warning("实时重复任务，请稍后再试！requestId:{}", msg["request_id"])
-            return
-        model_type = self.__context["service"]["model"]["model_type"]
-        codes = [model.get("code") for model in msg["models"] if model.get("code")]
-        if ModelMethodTypeEnum.NORMAL.value == model_type or ModelType.ILLPARKING_MODEL.value[1] in codes:
-            coir = OnlineIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
-        else:
-            coir = OnlineIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
-        coir.start()
-        logger.info("开始实时进程！requestId:{},pid:{}, ppid:{}",  msg["request_id"],os.getpid(),os.getppid())
-        self.__listeningProcesses[msg["request_id"]] = coir
-
-    # 结束实时进程
-    def stopOnlineProcess(self, msg):
-        ps = self.__listeningProcesses.get(msg["request_id"])
-        if ps is None:
-            logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
-            return
-        ps.sendEvent({"command": "stop"})
-        
-    # 新增该函数用于，向子任务发送命令（algStart，algStop）
-    def sendCmdToChildProcess(self, msg,cmd="algStart"):
-        ps = self.__listeningProcesses.get(msg["request_id"])
-        if ps is None:
-            logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
-            return
-        ps.sendEvent({"command": cmd})    
-
-    @staticmethod
-    def check_process(listeningProcess):
-        for requestId in list(listeningProcess.keys()):
-            if not listeningProcess[requestId].is_alive():
-                del listeningProcess[requestId]
-
-    def check_process_task(self):
-        self.check_process(self.__listeningProcesses)
-        self.check_process(self.__recordingProcesses)
-        self.check_process(self.__pull2PushProcesses)
-
-    # 开启离线进程
-    def startOfflineProcess(self, msg, analysisType):
-        if self.__listeningProcesses.get(msg["request_id"]):
-            logger.warning("离线重复任务，请稍后再试！requestId:{}", msg["request_id"])
-            return
-        model_type = self.__context["service"]["model"]["model_type"]
-        codes = [model.get("code") for model in msg["models"] if model.get("code")]
-        if ModelMethodTypeEnum.NORMAL.value == model_type:
-            first = OfflineIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
-        else:
-            first = OfflineIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
-        first.start()
-        self.__listeningProcesses[msg["request_id"]] = first
-
-    # 结束离线进程
-    def stopOfflineProcess(self, msg):
-        ps = self.__listeningProcesses.get(msg["request_id"])
-        if ps is None:
-            logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
-            return
-        ps.sendEvent({"command": "stop"})
-
-    # 开启图片分析进程
-    def startImageProcess(self, msg, analysisType):
-        pp = self.__listeningProcesses.get(msg["request_id"])
-        if pp is not None:
-            logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
-            return
-        model_type = self.__context["service"]["model"]["model_type"]
-        codes = [model.get("code") for model in msg["models"] if model.get("code")]
-        if ModelMethodTypeEnum.NORMAL.value == model_type or ModelType.ILLPARKING_MODEL.value[1] in codes:
-            imaged = PhotosIntelligentRecognitionProcess(self.__fbQueue, msg, analysisType, self.__context)
-        else:
-            imaged = PhotosIntelligentRecognitionProcess2(self.__fbQueue, msg, analysisType, self.__context)
-        # 创建在线识别进程并启动
-        imaged.start()
-        self.__listeningProcesses[msg["request_id"]] = imaged
-
-    '''
-        校验kafka消息
-    '''
-
-    @staticmethod
-    def check_msg(msg, schema):
-        try:
-            v = Validator(schema, allow_unknown=True)
-            result = v.validate(msg)
-            if not result:
-                logger.error("参数校验异常: {}, requestId: {}", v.errors, msg["request_id"])
-                raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
-                                       ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
-        except ServiceException as s:
-            raise s
-        except Exception:
-            logger.error("参数校验异常: {}, requestId: {}", format_exc(), msg["request_id"])
-            raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
-                                   ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
-
-    '''
-        开启反馈线程，用于发送消息
-    '''
-
-    def start_feedback_thread(self):
-        if self.__feedbackThread is None:
-            self.__feedbackThread = FeedbackThread(self.__fbQueue, self.__kafka_config)
-            self.__feedbackThread.setDaemon(True)
-            self.__feedbackThread.start()
-            time.sleep(1)
-        if self.__feedbackThread and not self.__feedbackThread.is_alive():
-            logger.error("反馈线程异常停止, 开始重新启动反馈线程！！！！！")
-            self.__feedbackThread = FeedbackThread(self.__fbQueue, self.__kafka_config)
-            self.__feedbackThread.setDaemon(True)
-            self.__feedbackThread.start()
-            time.sleep(1)
-
-    def start_uploadGPU_thread(self):
-        if self.__uploadGPUThread is None:
-            self.__uploadGPUThread = uploadGPUinfos(self.__context, self.__kafka_config)
-            self.__uploadGPUThread.setDaemon(True)
-            self.__uploadGPUThread.start()
-            time.sleep(1)
-        if self.__uploadGPUThread and not self.__uploadGPUThread.is_alive():
-            logger.error("反馈线程异常停止, 开始重新启动反馈线程！！！！！")
-            self.__uploadGPUThread = uploadGPUinfos(self.__context, self.__kafka_config)
-            self.__uploadGPUThread.setDaemon(True)
-            self.__uploadGPUThread.start()
-            time.sleep(1)
-    
-    '''
-    在线分析逻辑
-    '''
-
-    def online0(self, message, analysisType):
-        if "start" == message.get("command"):
-            self.check_msg(message, ONLINE_START_SCHEMA)
-            if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
-                raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
-                                       ExceptionType.NO_RESOURCES.value[1])
-            self.startOnlineProcess(message, analysisType)
-        elif  message.get("command") in ["algStart","algStop"]: 
-            self.sendCmdToChildProcess(message,cmd=message.get("command"))
-        elif "stop" == message.get("command"):
-            self.check_msg(message, ONLINE_STOP_SCHEMA)
-            self.stopOnlineProcess(message)
-        else:
-            raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
-                                   ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
-
-
-    def online(self, message, analysisType):
-        if "start" == message.get("command"):
-            self.check_msg(message, ONLINE_START_SCHEMA)
-            if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
-                raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
-                                       ExceptionType.NO_RESOURCES.value[1])
-            self.startOnlineProcess(message, analysisType)
-            
-        elif  message.get("command") in ["algStart","algStop"]: 
-            
-            if message.get("defaultEnabled",True):
-                self.sendCmdToChildProcess(message,cmd=message.get("command"))  
-                
-              
-        elif "stop" == message.get("command"):
-            self.check_msg(message, ONLINE_STOP_SCHEMA)
-            self.stopOnlineProcess(message)
-        else:
-            raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
-                                   ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
-
-
-
-
-    def offline(self, message, analysisType):
-        if "start" == message.get("command"):
-            self.check_msg(message, OFFLINE_START_SCHEMA)
-            if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["limit"]):
-                raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
-                                       ExceptionType.NO_RESOURCES.value[1])
-            self.startOfflineProcess(message, analysisType)
-        elif  message.get("command") in ["algStart","algStop"]: 
-            self.sendCmdToChildProcess( message,cmd=message.get("command"))    
-        elif "stop" == message.get("command"):
-            self.check_msg(message, OFFLINE_STOP_SCHEMA)
-            self.stopOfflineProcess(message)
-        else:
-            raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
-                                   ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
-
-    def image(self, message, analysisType):
-        if "start" == message.get("command"):
-            self.check_msg(message, IMAGE_SCHEMA)
-            if len(self.__listeningProcesses) >= int(self.__context['service']["task"]["image"]["limit"]):
-                raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
-                                       ExceptionType.NO_RESOURCES.value[1])
-            self.startImageProcess(message, analysisType)
-        else:
-            raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
-                                   ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
-
-    def recording(self, message, analysisType):
-        if "start" == message.get("command"):
-            self.check_msg(message, RECORDING_START_SCHEMA)
-            if len(self.__recordingProcesses) >= int(self.__context['service']["task"]["limit"]):
-                raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
-                                       ExceptionType.NO_RESOURCES.value[1])
-            self.startRecordingProcess(message, analysisType)
-        elif "stop" == message.get("command"):
-            self.check_msg(message, RECORDING_STOP_SCHEMA)
-            self.stopRecordingProcess(message)
-        else:
-            raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
-                                   ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
-
-    # 开启录屏进程
-    def startRecordingProcess(self, msg, analysisType):
-        if self.__listeningProcesses.get(msg["request_id"]):
-            logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
-            return
-        srp = ScreenRecordingProcess(self.__fbQueue, self.__context, msg, analysisType)
-        srp.start()
-        self.__recordingProcesses[msg["request_id"]] = srp
-
-    # 结束录屏进程
-    def stopRecordingProcess(self, msg):
-        rdp = self.__recordingProcesses.get(msg["request_id"])
-        if rdp is None:
-            logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
-            return
-        rdp.sendEvent({"command": "stop"})
-
-    def pullStream(self, message, analysisType):
-        if "start" == message.get("command"):
-            self.check_msg(message, PULL2PUSH_START_SCHEMA)
-            if len(self.__pull2PushProcesses) >= int(self.__context['service']["task"]["limit"]):
-                raise ServiceException(ExceptionType.NO_RESOURCES.value[0],
-                                       ExceptionType.NO_RESOURCES.value[1])
-
-            self.startPushStreamProcess(message, analysisType)
-        elif "stop" == message.get("command"):
-            self.check_msg(message, PULL2PUSH_STOP_SCHEMA)
-            self.stopPushStreamProcess(message)
-        else:
-            raise ServiceException(ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[0],
-                                   ExceptionType.ILLEGAL_PARAMETER_FORMAT.value[1])
-
-    def startPushStreamProcess(self, msg, analysisType):
-        if self.__pull2PushProcesses.get(msg["request_id"]):
-            logger.warning("重复任务，请稍后再试！requestId:{}", msg["request_id"])
-            return
-        srp = PushStreamProcess(self.__fbQueue, self.__context, msg, analysisType)
-        srp.start()
-        self.__pull2PushProcesses[msg["request_id"]] = srp
-
-    # 结束录屏进程
-    def stopPushStreamProcess(self, msg):
-        srp = self.__pull2PushProcesses.get(msg["request_id"])
-        if srp is None:
-            logger.warning("未查询到该任务，无法停止任务！requestId:{}", msg["request_id"])
-            return
-        srp.sendEvent({"command": "stop", "videoIds": msg.get("video_ids", [])})

util/ModelUtils.py

@@ -46,8 +46,12 @@ 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:
@@ -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 +756,11 @@ 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)
+    ),
 }

util/PlotsUtils.py

@@ -2,6 +2,7 @@ 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") 
@@ -67,6 +68,12 @@ def xywh2xyxy2(param):
         # return [(lt, yt), (rt, yt), (rt, yr), (lt, yr)]
     return np.asarray(param[0][0:4], np.int32), float(param[1]), int(param[2])
 
+def xy2xyxy(box):
+    if not isinstance(box[0], (list, tuple, np.ndarray)):
+        x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
+        # 顺时针
+        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):
     # 识别问题描述图片的高、宽
@@ -218,6 +225,11 @@ 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, 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, 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
@@ -251,8 +263,108 @@ 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
-    return det0_copy
+    return det0_copy
+
+def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
+    # print("省略 :%s, box:%s"%('+++' * 10, box))
+    # 识别问题描述图片的高、宽
+    lh, lw = label_array.shape[0:2]
+    # 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]
+    box = xy2xyxy(box)
+    # 框框左上的位置
+    x0, y1 = box[0][0], box[0][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 > 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
+    # 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
+    2. box（array）：为所画多边形的顶点坐标
+    3. 所画四边形是否闭合，通常为True
+    4. color（tuple）：BGR三个通道的值
+    5. thickness（int）：画线的粗细
+    6. shift：顶点坐标中小数的位数
+    '''
+    # Plots one bounding box on image img
+    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
+    box1 = np.asarray(box, np.int32)
+    cv2.polylines(img, [box1], True, color, tl)
+    img[y0:y1, x0:x1, :] = label_array
+
+    return img, box
+
+
+def draw_name_crowd(dets, img, color, outfontsize=20):
+    font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
+    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)
+
+        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
+
+            cv2.polylines(img, [np.asarray(xy2xyxy(b), np.int32)], True, (0, 128, 255), 2)
+            img[y0:y1, x0:x1, :] = label_arr
+
+
+    return img, dets