巴中水利分支

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。
-
-4、2025.07.10 周树亮 - 增加人群计数，自研车牌模型，裸土覆盖3个场景
-
-5、江朝庆 -- 0715
-1）代码整理，删除冗余代码。
-2）增加requirements.txt,方便部署
-3) logs

common/Constant.py

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

concurrency/FileUploadThread.py

@@ -3,7 +3,6 @@ from concurrent.futures import ThreadPoolExecutor
 from threading import Thread
 from time import sleep, time
 from traceback import format_exc
-import numpy as np
 
 from loguru import logger
 import cv2
@@ -15,18 +14,18 @@ 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, draw_name_ocr, draw_name_crowd,draw_transparent_red_polygon
+from util.PlotsUtils import draw_painting_joint, draw_name_ocr, draw_name_crowd
 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')
+    __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 = 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
 
@@ -65,29 +64,16 @@ class ImageFileUpload(FileUpload):
         模型编号：modeCode
         检测目标：detectTargetCode
         '''
-        aFrame = frame.copy()
-        igH, igW = aFrame.shape[0:2]
+        print('*' * 100, ' mqtt_list:', len(self._mqtt_list))
+
         model_info = []
-        mqttPares= det_xywh['mqttPares']
-        border = None
-        gps = [None, None]
-        camParas = None
-        if mqttPares is not None:
-            if mqttPares[0] == 1:
-                border = mqttPares[1]
-            elif mqttPares[0] == 0:
-                camParas = mqttPares[1]
-        if border is not None:
-            aFrame = draw_transparent_red_polygon(aFrame, np.array(border, np.int32), alpha=0.25)
-        det_xywh.pop('mqttPares')
         # 更加模型编码解析数据
         for code, det_list in det_xywh.items():
             if len(det_list) > 0:
                 for cls, target_list in det_list.items():
                     if len(target_list) > 0:
+                        aFrame = frame.copy()
                         for target in target_list:
-                            if camParas is not None:
-                                gps = locate_byMqtt(target[1], igW, igH, camParas, outFormat='wgs84')
                             # 自研车牌模型判断
                             if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
                                 draw_name_ocr(target[1], aFrame, target[4])
@@ -96,7 +82,15 @@ class ImageFileUpload(FileUpload):
                                 draw_name_crowd(target[1], aFrame, target[4])
                             else:
                                 draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config,
-                                                    target[5],border)
+                                                    target[5])
+
+                        igH, igW = aFrame.shape[0:2]
+                        if len(self._mqtt_list) >= 1:
+                            # camParas = self._mqtt_list[0]['data']
+                            camParas = self._mqtt_list[0]
+                            gps = locate_byMqtt(target[1], igW, igH, camParas, outFormat='wgs84')
+                        else:
+                            gps = [None, None]
                         model_info.append(
                             {"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame, 'gps': gps})
         if len(model_info) > 0:
@@ -139,6 +133,7 @@ class ImageFileUpload(FileUpload):
                         # 获取队列中的消息
                         image_msg = get_no_block_queue(image_queue)
                         if image_msg is not None:
+
                             if image_msg[0] == 2:
                                 logger.info("图片上传线程收到命令:{}, requestId: {}", image_msg[1], request_id)
                                 if 'stop' == image_msg[1]:

concurrency/FileUploadThread_20250106.py

@@ -0,0 +1,305 @@
+# -*- 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

@@ -46,7 +46,9 @@ from util.PlotsUtils import xywh2xyxy2
 from util.QueUtil import put_queue, get_no_block_queue, clear_queue
 from util.TimeUtils import now_date_to_str, YMDHMSF
 from util.CpuUtils import print_cpu_status
-import inspect 
+import inspect
+
+
 class IntelligentRecognitionProcess(Process):
     __slots__ = ('_fb_queue', '_msg', '_analyse_type', '_context', 'event_queue', '_pull_queue', '_hb_queue',
                  "_image_queue", "_push_queue", '_push_ex_queue')
@@ -111,8 +113,8 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
         pullProcess.start()
         return pullProcess
 
-    def upload_video(self,base_dir, env, request_id, orFilePath, aiFilePath):
-        if self._storage_source==1:
+    def upload_video(self, base_dir, env, request_id, orFilePath, aiFilePath):
+        if self._storage_source == 1:
             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)
@@ -237,7 +239,7 @@ 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)
 
@@ -247,7 +249,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
             # 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)
+            # print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
             # 第一个参数: 模型是否初始化 0:未初始化 1:初始化
             # 第二个参数: 检测是否有问题 0: 没有问题, 1: 有问题
             task_status = [0, 0]
@@ -263,7 +265,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                      pull_queue, request_id)
                         # 检查推流是否异常
                         push_status = get_no_block_queue(push_ex_queue)
-                        #print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #9.5,11.2
+                        # print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #9.5,11.2
                         if push_status is not None and push_status[0] == 1:
                             raise ServiceException(push_status[1], push_status[2])
                         # 获取停止指令
@@ -271,9 +273,9 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
 
                         if event_result:
                             cmdStr = event_result.get("command")
-                            #接收到算法开启、或者关闭的命令
-                            if cmdStr  in ['algStart' , 'algStop'  ]:
-                                logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id,cmdStr )
+                            # 接收到算法开启、或者关闭的命令
+                            if cmdStr in ['algStart', 'algStop']:
+                                logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id, cmdStr)
                                 put_queue(push_queue, (2, cmdStr), timeout=1, is_ex=True)
                                 pull_process.sendCommand({"command": cmdStr})
 
@@ -281,9 +283,9 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                             if "stop" == cmdStr:
                                 logger.info("实时任务开始停止, requestId: {}", request_id)
                                 pull_process.sendCommand({"command": 'stop'})
-                        #print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)          
+                        # print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
                         pull_result = get_no_block_queue(pull_queue)
-                        #print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)  
+                        # print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
                         if pull_result is None:
                             sleep(1)
                             continue
@@ -308,7 +310,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                         else:
                                             model_param = model_conf[1]
                                             # (modeType, model_param, allowedList, names, rainbows)
-                                            MODEL_CONFIG[code][2](frame_list[0][0].shape[1], frame_list[0][0].shape[0],
+                                            MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0],
                                                                   model_conf)
                                             if draw_config.get("font_config") is None:
                                                 draw_config["font_config"] = model_param['font_config']
@@ -324,7 +326,7 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                 # print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno)
                                 # 多线程并发处理, 经过测试两个线程最优
                                 det_array = []
-                                for i, [frame,_] in enumerate(frame_list):
+                                for i, frame in enumerate(frame_list):
                                     det_result = t.submit(self.obj_det, self, model_array, frame, task_status,
                                                           frame_index_list[i], tt, request_id)
                                     det_array.append(det_result)
@@ -606,8 +608,8 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                             if "stop" == cmdStr:
                                 logger.info("离线任务开始停止, requestId: {}", request_id)
                                 pull_process.sendCommand({"command": 'stop'})
-                            if cmdStr  in ['algStart' , 'algStop'  ]:
-                                logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id,cmdStr )
+                            if cmdStr in ['algStart', 'algStop']:
+                                logger.info("发送向推流进程发送算法命令, requestId: {}, {}", request_id, cmdStr)
                                 put_queue(push_queue, (2, cmdStr), timeout=1, is_ex=True)
                                 pull_process.sendCommand({"command": cmdStr})
 
@@ -637,7 +639,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                         else:
                                             model_param = model_conf[1]
                                             # (modeType, model_param, allowedList, names, rainbows)
-                                            MODEL_CONFIG[code][2](frame_list[0][0].shape[1], frame_list[0][0].shape[0],
+                                            MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0],
                                                                   model_conf)
                                             if draw_config.get("font_config") is None:
                                                 draw_config["font_config"] = model_param['font_config']
@@ -651,7 +653,7 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
 
 
                                 det_array = []
-                                for i, [frame,_] in enumerate(frame_list):
+                                for i, frame in enumerate(frame_list):
                                     det_result = t.submit(self.obj_det, self, model_array, frame, task_status,
                                                           frame_index_list[i], tt, request_id)
                                     det_array.append(det_result)
@@ -971,7 +973,7 @@ class PhotosIntelligentRecognitionProcess(Process):
             logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id)
             raise e
 
-    #密集人群计数
+    # 密集人群计数
     def denscrowdcount_rec(self, imageUrl, mod, image_queue, request_id):
         try:
             # model_conf： modeType, allowedList, detpar, ocrmodel, rainbows
@@ -1040,7 +1042,7 @@ class PhotosIntelligentRecognitionProcess(Process):
             image = url2Array(imageUrl)
             MODEL_CONFIG[code][2](image.shape[1], image.shape[0], model_conf)
             p_result = MODEL_CONFIG[code][3]([model_conf, image, request_id])[0]
-            #print(' line872:p_result[2]:',p_result[2] )
+            # print(' line872:p_result[2]:',p_result[2] )
             if p_result is None or len(p_result) < 3 or p_result[2] is None or len(p_result[2]) == 0:
                 return
             if logo:
@@ -1054,8 +1056,6 @@ class PhotosIntelligentRecognitionProcess(Process):
             ai_result_list = p_result[2]
             for ai_result in ai_result_list:
                 box, score, cls = xywh2xyxy2(ai_result)
-                if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
-                    box.append(ai_result[-1])
                 # 如果检测目标在识别任务中,继续处理
                 if cls in allowedList:
                     label_array = label_arraylist[cls]
@@ -1065,7 +1065,7 @@ class PhotosIntelligentRecognitionProcess(Process):
                         det_xywh[code][cls] = [[cls, box, score, label_array, color]]
                     else:
                         det_xywh[code][cls].append([cls, box, score, label_array, color])
-            #print('ai_result_list:{},allowlist:{}'.format(ai_result_list,allowedList    ))            
+            # print('ai_result_list:{},allowlist:{}'.format(ai_result_list,allowedList    ))
             if len(det_xywh) > 0:
                 put_queue(image_queue, (1, (det_xywh, imageUrl, image, font_config, "")), timeout=2, is_ex=False)
         except ServiceException as s:
@@ -1219,12 +1219,12 @@ class PhotosIntelligentRecognitionProcess(Process):
             # 发送 HTTP 请求，尝试访问图片
             response = requests.get(url, timeout=timeout)  # 设置超时时间为 10 秒
             if response.status_code == 200:
-                return True,url
+                return True, url
             else:
-                return False,f"图片地址无效，状态码：{response.status_code}"
+                return False, f"图片地址无效，状态码：{response.status_code}"
         except requests.exceptions.RequestException as e:
             # 捕获请求过程中可能出现的异常（如网络问题、超时等）
-            return False,str(e)
+            return False, str(e)
 
     def run(self):
         fb_queue, msg, analyse_type, context = self._fb_queue, self._msg, self._analyse_type, self._context
@@ -1233,23 +1233,23 @@ class PhotosIntelligentRecognitionProcess(Process):
         imageUrls = msg["image_urls"]
         image_thread = None
         init_log(base_dir, env)
-        valFlag=True
+        valFlag = True
         for url in imageUrls:
-            valFlag,ret = self.check_ImageUrl_Vaild(url,timeout=1)
+            valFlag, ret = self.check_ImageUrl_Vaild(url, timeout=1)
 
             if not valFlag:
-                logger.error("图片分析异常: {}, requestId:{},url:{}",ret, request_id,url)
-                #print("AnalysisStatus.FAILED.value:{}，ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0]:{},ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1]:{}".format(AnalysisStatus.FAILED.value,ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1] )  )
+                logger.error("图片分析异常: {}, requestId:{},url:{}", ret, request_id, url)
+                # print("AnalysisStatus.FAILED.value:{}，ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0]:{},ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1]:{}".format(AnalysisStatus.FAILED.value,ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1] )  )
                 put_queue(fb_queue, message_feedback(request_id, AnalysisStatus.FAILED.value,
-                                                 analyse_type,
-                                                 ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],
-                                                 ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1]), timeout=2) 
+                                                     analyse_type,
+                                                     ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[0],
+                                                     ExceptionType.URL_ADDRESS_ACCESS_FAILED.value[1]), timeout=2)
 
                 return
 
         with ThreadPoolExecutor(max_workers=1) as t:
             try:
-                #init_log(base_dir, env)
+                # init_log(base_dir, env)
                 logger.info("开始启动图片识别进程, requestId: {}", request_id)
                 model_array = get_model(msg, context, analyse_type)
                 image_thread = self.start_File_upload(fb_queue, context, msg, image_queue, analyse_type)
@@ -1513,6 +1513,7 @@ class ScreenRecordingProcess(Process):
         return or_url
     '''
 
+
 """
 "models": [{
     "code": "模型编号",

concurrency/PullMqttThread.py

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

concurrency/PullVideoStreamProcess.py

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

concurrency/PushVideoStreamProcess.py

@@ -1,4 +1,4 @@
-#ne -*- coding: utf-8 -*-
+# ne -*- coding: utf-8 -*-
 
 from concurrent.futures import ThreadPoolExecutor
 from multiprocessing import Process
@@ -23,7 +23,8 @@ from util.Cv2Utils import video_conjuncing, write_or_video, write_ai_video, push
 from util.ImageUtils import url2Array, add_water_pic
 from util.LogUtils import init_log
 
-from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, xy2xyxy, draw_name_joint, plot_one_box_auto, draw_name_ocr,draw_name_crowd,draw_transparent_red_polygon
+from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, xy2xyxy, draw_name_joint, plot_one_box_auto, \
+    draw_name_ocr, draw_name_crowd
 
 from util.QueUtil import get_no_block_queue, put_queue, clear_queue
 
@@ -35,7 +36,7 @@ class PushStreamProcess(Process):
         super().__init__()
         # 传参
         self._msg, self._push_queue, self._image_queue, self._push_ex_queue, self._hb_queue, self._context = args
-        self._algStatus = False # 默认关闭
+        self._algStatus = False  # 默认关闭
         self._algSwitch = self._context['service']['algSwitch']
 
         # 0521:
@@ -49,7 +50,7 @@ class PushStreamProcess(Process):
             # 这里放非默认逻辑的代码
             self._algSwitch = False
 
-        print("---line53 :PushVideoStreamProcess.py---",self._algSwitch)
+        print("---line53 :PushVideoStreamProcess.py---", self._algSwitch)
 
     def build_logo_url(self):
         logo = None
@@ -106,7 +107,7 @@ class OnPushStreamProcess(PushStreamProcess):
         pix_dis = 60
         try:
             init_log(base_dir, env)
-            logger.info("开始实时启动推流进程！requestId:{},pid:{}, ppid:{}", request_id,os.getpid(),os.getppid())
+            logger.info("开始实时启动推流进程！requestId:{},pid:{}, ppid:{}", request_id, os.getpid(), os.getppid())
             with ThreadPoolExecutor(max_workers=2) as t:
                 # 定义三种推流、写原视频流、写ai视频流策略
                 # 第一个参数时间, 第二个参数重试次数
@@ -130,33 +131,24 @@ class OnPushStreamProcess(PushStreamProcess):
                     if push_r is not None:
                         if push_r[0] == 1:
                             frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
-                            # 处理每1帧
-                            for i, [frame,mqtt_list] in enumerate(frame_list):
-                                # mqtt传参
-                                border = None
-                                mqttPares = None
-                                if len(mqtt_list) >= 1:
-                                    mqttPares = mqtt_list[0]
-                                    if mqttPares[0] == 1:
-                                        border = mqttPares[1]
-                                pix_dis = int((frame.shape[0]//10)*1.2)
+                            for i, frame in enumerate(frame_list):
+                                pix_dis = int((frame.shape[0] // 10) * 1.2)
                                 # 复制帧用来画图
                                 copy_frame = frame.copy()
-                                if border is not None:
-                                    copy_frame = draw_transparent_red_polygon(copy_frame, np.array(border, np.int32),alpha=0.25)
                                 det_xywh, thread_p = {}, []
-                                det_xywh2 = {'mqttPares':mqttPares}
+                                det_xywh2 = {}
                                 # 所有问题的矩阵集合
                                 qs_np = None
                                 qs_reurn = []
-                                bp_np = None
                                 for det in push_objs[i]:
                                     code, det_result = det
                                     # 每个单独模型处理
                                     # 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
                                     if len(det_result) > 0:
-                                        font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
-                                        rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
+                                        font_config, allowedList = draw_config["font_config"], draw_config[code][
+                                            "allowedList"]
+                                        rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code][
+                                            "label_arrays"]
                                         for qs in det_result:
                                             # 自研车牌模型处理
                                             if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
@@ -178,55 +170,45 @@ class OnPushStreamProcess(PushStreamProcess):
                                             else:
                                                 try:  # 应对NaN情况
                                                     box, score, cls = xywh2xyxy2(qs)
-                                                    if cls not in allowedList or score < frame_score:
-                                                        continue
-                                                    if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
-                                                        # 借score作为points点集
-                                                        box.append(qs[-1])
                                                 except:
                                                     continue
+                                                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, border=border)
-
+                                                    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:
                                                 det_xywh[code] = {}
                                             cd = det_xywh[code].get(cls)
-                                            if not (ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2):
+                                            if not (ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2) :
                                                 if cd is None:
                                                     det_xywh[code][cls] = [[cls, box, score, label_array, color]]
                                                 else:
                                                     det_xywh[code][cls].append([cls, box, score, label_array, color])
                                                 if qs_np is None:
                                                     qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
-                                                                    box[2][0], box[2][1], box[3][0], box[3][1],
-                                                                    score, cls, code],dtype=np.float32)
+                                                                      box[2][0], box[2][1], box[3][0], box[3][1],
+                                                                      score, cls, code], dtype=np.float32)
                                                 else:
                                                     result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
-                                                                        box[2][0], box[2][1], box[3][0], box[3][1],
-                                                                        score, cls, code],dtype=np.float32)
+                                                                          box[2][0], box[2][1], box[3][0], box[3][1],
+                                                                          score, cls, code], dtype=np.float32)
                                                     qs_np = np.row_stack((qs_np, result_li))
 
-                                            if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
-                                                if bp_np is None:
-                                                    bp_np = np.array([box[0][0], box[0][1], box[-1]], dtype=object)
-                                                else:
-                                                    bp_li = np.array([box[0][0], box[0][1], box[-1]], dtype=object)
-                                                    bp_np = np.row_stack((bp_np, bp_li))
-
                                 if logo:
                                     frame = add_water_pic(frame, logo, request_id)
                                     copy_frame = add_water_pic(copy_frame, logo, request_id)
                                 if len(thread_p) > 0:
                                     for r in thread_p:
                                         r.result()
-                                #print('----line173:',self._algSwitch,self._algStatus)
+                                # print('----line173:',self._algSwitch,self._algStatus)
                                 if self._algSwitch and (not self._algStatus):
                                     frame_merge = video_conjuncing(frame, frame.copy())
                                 else:
@@ -242,19 +224,19 @@ class OnPushStreamProcess(PushStreamProcess):
                                 # 如果有问题, 走下面的逻辑
                                 if qs_np is not None:
                                     if len(qs_np.shape) == 1:
-                                        qs_np = qs_np[np.newaxis,...]
+                                        qs_np = qs_np[np.newaxis, ...]
                                     qs_np_id = qs_np.copy()
                                     b = np.ones(qs_np_id.shape[0])
-                                    qs_np_id = np.column_stack((qs_np_id,b))
+                                    qs_np_id = np.column_stack((qs_np_id, b))
                                     if qs_np_tmp is None:
                                         if picture_similarity:
                                             qs_np_tmp = qs_np_id.copy()
                                         b = np.zeros(qs_np.shape[0])
-                                        qs_reurn = np.column_stack((qs_np,b))
+                                        qs_reurn = np.column_stack((qs_np, b))
                                     else:
                                         qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
                                         if picture_similarity:
-                                            qs_np_tmp = np.append(qs_np_tmp,qs_np_id,axis=0)
+                                            qs_np_tmp = np.append(qs_np_tmp, qs_np_id, axis=0)
                                     qs_np_tmp[:, 11] += 1
                                     qs_np_tmp = np.delete(qs_np_tmp, np.where((qs_np_tmp[:, 11] >= 75))[0], axis=0)
                                 has = False
@@ -274,16 +256,11 @@ class OnPushStreamProcess(PushStreamProcess):
                                                 det_xywh2[code] = {}
                                             cd = det_xywh2[code].get(cls)
                                             score = q[8]
-                                            rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
+                                            rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code][
+                                                "label_arrays"]
                                             label_array, color = label_arrays[cls], rainbows[cls]
                                             box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
                                                    (int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
-                                            if bp_np is not None:
-                                                if len(bp_np.shape)==1:
-                                                    bp_np = bp_np[np.newaxis, ...]
-                                                for bp in bp_np:
-                                                    if np.array_equal(bp[:2], np.array([int(q[0]), int(q[1])])):
-                                                        box.append(bp[-1])
                                             is_new = False
                                             if q[11] == 1:
                                                 is_new = True
@@ -305,9 +282,11 @@ class OnPushStreamProcess(PushStreamProcess):
                                 or_video_file = write_or_video_result.result(timeout=60)
                         # 接收停止指令
                         if push_r[0] == 2:
-                            logger.info("拉流进程收到控制命令为:{}, requestId: {}",push_r[1] ,request_id)
-                            if 'algStart' == push_r[1]: self._algStatus = True;logger.info("算法识别开启, requestId: {}", request_id)
-                            if 'algStop' == push_r[1]:  self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
+                            logger.info("拉流进程收到控制命令为:{}, requestId: {}", push_r[1], request_id)
+                            if 'algStart' == push_r[1]: self._algStatus = True;logger.info(
+                                "算法识别开启, requestId: {}", request_id)
+                            if 'algStop' == push_r[1]:  self._algStatus = False;logger.info(
+                                "算法识别关闭, requestId: {}", request_id)
                             if 'stop' == push_r[1]:
                                 logger.info("停止推流进程, requestId: {}", request_id)
                                 break
@@ -364,12 +343,14 @@ class OffPushStreamProcess(PushStreamProcess):
         picture_similarity = bool(context["service"]["filter"]["picture_similarity"])
         qs_np_tmp = None
         pix_dis = 60
-        if msg['taskType']==0: self._algStatus = False
-        else: self._algStatus = True
+        if msg['taskType'] == 0:
+            self._algStatus = False
+        else:
+            self._algStatus = True
         try:
             init_log(base_dir, env)
             logger.info("开始启动离线推流进程！requestId:{}", request_id)
-            with ThreadPoolExecutor(max_workers=2) as t:
+            with (ThreadPoolExecutor(max_workers=2) as t):
                 # 定义三种推流、写原视频流、写ai视频流策略
                 # 第一个参数时间, 第二个参数重试次数
                 p_push_status, ai_write_status = [0, 0], [0, 0]
@@ -394,38 +375,32 @@ class OffPushStreamProcess(PushStreamProcess):
                         # [(2, 操作指令)] 指令操作
                         if push_r[0] == 1:
                             frame_list, frame_index_list, all_frames, draw_config, push_objs = push_r[1]
-
                             # 处理每一帧图片
-                            for i, [frame,mqtt_list] in enumerate(frame_list):
-                                # mqtt传参
-                                border = None
-                                mqttPares = None
-                                if len(mqtt_list) >= 1:
-                                    mqttPares = mqtt_list[0]
-                                    if mqttPares[0] == 1:
-                                        border = mqttPares[1]
-                                pix_dis = int((frame.shape[0]//10)*1.2)
+                            # 每100帧上传一次
+                            ncount = 0
+                            for i, frame in enumerate(frame_list):
+                                pix_dis = int((frame.shape[0] // 10) * 1.2)
                                 if frame_index_list[i] % 300 == 0 and frame_index_list[i] <= all_frames:
                                     task_process = "%.2f" % (float(frame_index_list[i]) / float(all_frames))
                                     put_queue(hb_queue, {"hb_value": task_process}, timeout=2)
                                 # 复制帧用来画图
                                 copy_frame = frame.copy()
-                                if border is not None:
-                                    copy_frame = draw_transparent_red_polygon(copy_frame, np.array(border, np.int32),alpha=0.25)
                                 # 所有问题记录字典
                                 det_xywh, thread_p = {}, []
-                                det_xywh2 = {'mqttPares':mqttPares}
+                                det_xywh2 = {}
                                 # 所有问题的矩阵集合
                                 qs_np = None
                                 qs_reurn = []
-                                bp_np = None
                                 for det in push_objs[i]:
                                     code, det_result = det
+
                                     # 每个单独模型处理
                                     # 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
                                     if len(det_result) > 0:
-                                        font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
-                                        rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
+                                        font_config, allowedList = draw_config["font_config"], draw_config[code][
+                                            "allowedList"]
+                                        rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code][
+                                            "label_arrays"]
                                         for qs in det_result:
                                             # 自研车牌模型处理
                                             if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
@@ -450,16 +425,17 @@ class OffPushStreamProcess(PushStreamProcess):
                                                 box, score, cls = xywh2xyxy2(qs)
                                                 if cls not in allowedList or score < frame_score:
                                                     continue
-                                                if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
-                                                    box.append(qs[-1])
-
                                                 label_array, color = label_arrays[cls], rainbows[cls]
                                                 if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
-                                                    rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
+                                                    rr = t.submit(draw_name_joint, box, copy_frame,
+                                                                  draw_config[code]["label_dict"], score, color,
+                                                                  font_config, qs[6])
                                                 else:
-                                                    rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config, border=border)
+                                                    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:
                                                 det_xywh[code] = {}
                                             cd = det_xywh[code].get(cls)
@@ -470,21 +446,14 @@ class OffPushStreamProcess(PushStreamProcess):
                                                     det_xywh[code][cls].append([cls, box, score, label_array, color])
                                                 if qs_np is None:
                                                     qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
-                                                                    box[2][0], box[2][1], box[3][0], box[3][1],
-                                                                    score, cls, code],dtype=np.float32)
+                                                                      box[2][0], box[2][1], box[3][0], box[3][1],
+                                                                      score, cls, code], dtype=np.float32)
                                                 else:
                                                     result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
-                                                                        box[2][0], box[2][1], box[3][0], box[3][1],
-                                                                        score, cls, code],dtype=np.float32)
+                                                                          box[2][0], box[2][1], box[3][0], box[3][1],
+                                                                          score, cls, code], dtype=np.float32)
                                                     qs_np = np.row_stack((qs_np, result_li))
 
-                                                if ModelType.CITY_FIREAREA_MODEL.value[1]== str(code):
-                                                    if bp_np is None:
-                                                        bp_np = np.array([box[0][0], box[0][1],box[-1]],dtype=object)
-                                                    else:
-                                                        bp_li = np.array([box[0][0], box[0][1],box[-1]],dtype=object)
-                                                        bp_np = np.row_stack((bp_np, bp_li))
-
                                 if logo:
                                     frame = add_water_pic(frame, logo, request_id)
                                     copy_frame = add_water_pic(copy_frame, logo, request_id)
@@ -504,19 +473,19 @@ class OffPushStreamProcess(PushStreamProcess):
 
                                 if qs_np is not None:
                                     if len(qs_np.shape) == 1:
-                                        qs_np = qs_np[np.newaxis,...]
+                                        qs_np = qs_np[np.newaxis, ...]
                                     qs_np_id = qs_np.copy()
                                     b = np.ones(qs_np_id.shape[0])
-                                    qs_np_id = np.column_stack((qs_np_id,b))
+                                    qs_np_id = np.column_stack((qs_np_id, b))
                                     if qs_np_tmp is None:
                                         if picture_similarity:
                                             qs_np_tmp = qs_np_id.copy()
                                         b = np.zeros(qs_np.shape[0])
-                                        qs_reurn = np.column_stack((qs_np,b))
+                                        qs_reurn = np.column_stack((qs_np, b))
                                     else:
                                         qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
                                         if picture_similarity:
-                                            qs_np_tmp = np.append(qs_np_tmp,qs_np_id,axis=0)
+                                            qs_np_tmp = np.append(qs_np_tmp, qs_np_id, axis=0)
                                     qs_np_tmp[:, 11] += 1
                                     qs_np_tmp = np.delete(qs_np_tmp, np.where((qs_np_tmp[:, 11] >= 75))[0], axis=0)
                                 has = False
@@ -537,16 +506,11 @@ class OffPushStreamProcess(PushStreamProcess):
                                                 det_xywh2[code] = {}
                                             cd = det_xywh2[code].get(cls)
                                             score = q[8]
-                                            rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
+                                            rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code][
+                                                "label_arrays"]
                                             label_array, color = label_arrays[cls], rainbows[cls]
                                             box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
                                                    (int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
-                                            if bp_np is not None:
-                                                if len(bp_np.shape)==1:
-                                                    bp_np = bp_np[np.newaxis, ...]
-                                                for bp in bp_np:
-                                                    if np.array_equal(bp[:2], np.array([int(q[0]), int(q[1])])):
-                                                        box.append(bp[-1])
                                             is_new = False
                                             if q[11] == 1:
                                                 is_new = True
@@ -560,15 +524,18 @@ class OffPushStreamProcess(PushStreamProcess):
                                             else:
                                                 det_xywh2[code][cls].append(
                                                     [cls, box, score, label_array, color, is_new])
-                                    if len(det_xywh2) > 1:
-                                        put_queue(image_queue, (1, [det_xywh2, frame, frame_index_list[i], all_frames, draw_config["font_config"]]))
+                                    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)
                                 ai_video_file = write_ai_video_result.result(timeout=60)
                         # 接收停止指令
                         if push_r[0] == 2:
-                            logger.info("拉流进程收到控制命令为:{}, requestId: {}",push_r[1] ,request_id)
-                            if 'algStart' == push_r[1]: self._algStatus = True;logger.info("算法识别开启, requestId: {}", request_id)
-                            if 'algStop' == push_r[1]:  self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
+                            logger.info("拉流进程收到控制命令为:{}, requestId: {}", push_r[1], request_id)
+                            if 'algStart' == push_r[1]: self._algStatus = True;logger.info(
+                                "算法识别开启, requestId: {}", request_id)
+                            if 'algStop' == push_r[1]:  self._algStatus = False;logger.info(
+                                "算法识别关闭, requestId: {}", request_id)
                             if 'stop' == push_r[1]:
                                 logger.info("停止推流进程, requestId: {}", request_id)
                                 break

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: "15 days"
+retention: "7 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: "7 days"
+retention: "3 days"
 encoding: "utf8"
 

config/mqtt/dsp_test_mqtt.yml

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

config/service/dsp_prod_service.yml

@@ -3,6 +3,7 @@ video:
   file_path: "../dsp/video/"
   # 是否添加水印
   video_add_water: false
+role : 1
 service:
   filter:
     # 图片得分多少分以上返回图片

config/service/dsp_test_service.yml

@@ -33,8 +33,7 @@ service:
   #storage source,0--aliyun,1--minio    
   storage_source: 0
   #是否启用mqtt，0--不用，1--启用
-  mqtt:
-    flag: 0
-    business: 1
+  mqtt_flag: 0
   #是否启用alg控制功能
-  algSwitch: False
+  algSwitch: False
+

enums/ModelTypeEnum-jcq.py

@@ -1,768 +0,0 @@
-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

@@ -1,807 +0,0 @@
-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/ModelTypeEnum2.py

@@ -1,762 +0,0 @@
-import sys
-from enum import Enum, unique
-
-from common.Constant import COLOR
-
-sys.path.extend(['..', '../AIlib2'])
-from segutils.segmodel import SegModel
-from utilsK.queRiver import riverDetSegMixProcess_N
-from segutils.trafficUtils import tracfficAccidentMixFunction_N
-from utilsK.drownUtils import mixDrowing_water_postprocess_N
-from utilsK.noParkingUtils import mixNoParking_road_postprocess_N
-from utilsK.illParkingUtils import illParking_postprocess
-from DMPR import DMPRModel
-from DMPRUtils.jointUtil import dmpr_yolo
-from yolov5 import yolov5Model
-from stdc import stdcModel
-from AI import default_mix
-from DMPRUtils.jointUtil import  dmpr_yolo_stdc
-
-'''
-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
-'''
-
-
-@unique
-class ModelType2(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"],
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7]  ],###控制哪些检测类别显示、输出
-        'trackPar': {
-            'sort_max_age': 2,  # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
-            'sort_min_hits': 3,  # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
-            'sort_iou_thresh': 0.2,  # 检测最小的置信度。
-            'det_cnt': 10,  # 每隔几次做一个跟踪和检测，默认10。
-            'windowsize': 29,  # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
-            'patchCnt': 100,  # 每次送入图像的数量，不宜少于100帧。
-        },
-        'postProcess':{'function':riverDetSegMixProcess_N,'pars':{'slopeIndex':[1,3,4,7], 'riverIou':0.1}}, #分割和检测混合处理的函数
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 80,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'segLineShow': False,
-            'waterLineColor': (0, 255, 255),
-            'waterLineWidth': 3
-        },
-        'models':
-                [
-                    {
-                        'weight':"../AIlib2/weights/river/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  } },       
-                    },
-                    {
-                        'weight':'../AIlib2/weights/conf/river/stdc_360X640.pth',
-                        'par':{
-                            'modelSize':(640,360),
-                            'mean':(0.485, 0.456, 0.406),
-                            'std' :(0.229, 0.224, 0.225),
-                            'numpy':False, 
-                            'RGB_convert_first':True,
-                            'seg_nclass':2},###分割模型预处理参数
-                        'model':stdcModel,
-                        'name':'stdc'        
-                    }
-                        
-                ],
-    })
-
-    FOREST_FARM_MODEL = ("2", "002", "森林模型", 'forest2', lambda device, gpuName: {
-        'device': device,
-        'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾"],
-        '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  } 
-                               },       
-                    }                       
-                ],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-            'postProcess':{'function':default_mix,'pars':{ }},
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 80,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'segLineShow': False,
-            'waterLineColor': (0, 255, 255),
-            'waterLineWidth': 3
-        }
-    })
-
-    TRAFFIC_FARM_MODEL = ("3", "003", "交通模型", 'highWay2', lambda device, gpuName: {
-        'device': 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", "事故"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':5,'windowsize':29,'patchCnt':100},
-        'postProcess':{
-            'function':tracfficAccidentMixFunction_N,
-            'pars':{ 
-                'RoadArea': 16000, 
-                'vehicleArea': 10, 
-                'roadVehicleAngle': 15, 
-                'speedRoadVehicleAngleMax': 75,
-                'radius': 50 , 
-                'roundness': 1.0, 
-                'cls': 9, 
-                'vehicleFactor': 0.1,
-                'cls':9, 
-                'confThres':0.25,
-                'roadIou':0.6,
-                'vehicleFlag':False,
-                'distanceFlag': False,
-                'modelSize':(640,360), 
-            }
-        },
-        'models':
-            [
-                {
-                    'weight':"../AIlib2/weights/highWay2/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  } },       
-                },
-                {
-                    'weight':'../AIlib2/weights/conf/highWay2/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':3},###分割模型预处理参数
-                    'model':stdcModel,
-                    'name':'stdc'        
-                }    
-            ],
-        '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
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.25,
-            "classes": 9,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 20,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'waterLineColor': (0, 255, 255),
-            'segLineShow': False,
-            'waterLineWidth': 2
-        }
-    })
-
-    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,
-        'labelnames': ["车辆"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-        'postProcess':{'function':default_mix,'pars':{ }},
-        'models':
-            [
-                {
-                    'weight':"../AIlib2/weights/vehicle/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  } },       
-                }                    
-            ], 
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 40,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'waterLineColor': (0, 255, 255),
-            'segLineShow': False,
-            'waterLineWidth': 3
-        }
-    })
-
-    PEDESTRIAN_MODEL = ("7", "007", "行人模型", 'pedestrian', lambda device, gpuName: {
-        'device': device,
-        'labelnames': ["行人"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-        'postProcess':{'function':default_mix,'pars':{ }},
-        'models':
-            [
-                {
-                    'weight':"../AIlib2/weights/pedestrian/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  } },       
-                }
-            ], 
-        
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'segLineShow': False,
-            'waterLineColor': (0, 255, 255),
-            'waterLineWidth': 3
-        }
-    })
-
-    SMOGFIRE_MODEL = ("8", "008", "烟火模型", 'smogfire', lambda device, gpuName: {
-        'device': device,
-        'labelnames': ["烟雾", "火焰"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-            'postProcess':{'function':default_mix,'pars':{ }},
-            'models':
-                [
-                    {
-                        'weight':"../AIlib2/weights/smogfire/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
-                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
-                        '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  } },       
-                    }
-                ],  
-            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 40,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'segLineShow': False,
-            'waterLineColor': (0, 255, 255),
-            'waterLineWidth': 3
-        }
-    })
-
-    ANGLERSWIMMER_MODEL = ("9", "009", "钓鱼游泳模型", 'AnglerSwimmer', lambda device, gpuName: {
-        'device': device,
-        'labelnames': ["钓鱼", "游泳"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-        'postProcess':{'function':default_mix,'pars':{ }},
-        'models':
-            [
-                {
-                    'weight':"../AIlib2/weights/AnglerSwimmer/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  } },       
-                }
-            ],  
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 40,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'segLineShow': False,
-            'waterLineColor': (0, 255, 255),
-            'waterLineWidth': 3
-        },
-    })
-
-    COUNTRYROAD_MODEL = ("10", "010", "乡村模型", 'countryRoad', lambda device, gpuName: {
-        'device': device,
-        'labelnames': ["违法种植"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-        'postProcess':{'function':default_mix,'pars':{ }},
-        'models':
-            [
-                {
-                    'weight':"../AIlib2/weights/countryRoad/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  } },       
-                }
-            ],  
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 40,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'segLineShow': False,
-            'waterLineColor': (0, 255, 255),
-            'waterLineWidth': 3
-        }
-    })
-
-    SHIP_MODEL = ("11", "011", "船只模型", 'ship2', lambda device, gpuName: {
-        'obbModelPar': {
-            'labelnames': ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "船只"],
-            'model_size': (608, 608),
-            'K': 100,
-            'conf_thresh': 0.3,
-            'down_ratio': 4,
-            'num_classes': 15,
-            'dataset': 'dota',
-            'heads': {
-                'hm': None,
-                'wh': 10,
-                'reg': 2,
-                'cls_theta': 1
-            },
-            'mean': (0.5, 0.5, 0.5),
-            'std': (1, 1, 1),
-            'half': False,
-            'test_flag': True,
-            'decoder': None,
-            'weights': '../AIlib2/weights/ship2/obb_608X608_%s_fp16.engine' % gpuName
-        },
-        'trackPar': {
-            'sort_max_age': 2,  # 跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
-            'sort_min_hits': 3,  # 每隔目标连续出现的次数，超过这个次数才认为是一个目标。
-            'sort_iou_thresh': 0.2,  # 检测最小的置信度。
-            'det_cnt': 10,  # 每隔几次做一个跟踪和检测，默认10。
-            'windowsize': 29,  # 轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。一个目标在多个帧中出现，每一帧中都有一个位置，这些位置的连线交轨迹。
-            'patchCnt': 100,  # 每次送入图像的数量，不宜少于100帧。
-        },
-        'device': "cuda:%s" % device,
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'drawBox': False,
-        'drawPar': {
-            "rainbows": COLOR,
-            'digitWordFont': {
-                'line_thickness': 2,
-                'boxLine_thickness': 1,
-                'wordSize': 40,
-                'fontSize': 1.0,
-                'label_location': 'leftTop'
-            }
-        },
-        '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,
-        'labelnames': ["人"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-        'postProcess':{'function':default_mix,'pars':{ }},
-        'models':
-            [
-                {
-                    'weight':"../AIlib2/weights/channelEmergency/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
-                    #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
-                    '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  } },       
-                }
-            ],  
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in []  ],###控制哪些检测类别显示、输出
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 40,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'segLineShow': False,
-            'waterLineColor': (0, 255, 255),
-            'waterLineWidth': 3
-        }
-    })
-
-    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"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-        'postProcess':{'function':riverDetSegMixProcess_N,'pars':{'slopeIndex':[1,3,4,7], 'riverIou':0.1}}, #分割和检测混合处理的函数         
-        'models':
-            [
-                {
-                    'weight':"../AIlib2/weights/river2/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  } },       
-                },
-                {
-                    'weight':'../AIlib2/weights/conf/river2/stdc_360X640.pth',
-                    'par':{
-                        'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True,'seg_nclass':2},###分割模型预处理参数
-                    'model':stdcModel,
-                    'name':'stdc'        
-                }      
-            ],
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.3,
-            "ovlap_thres_crossCategory": 0.65,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 80,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'segLineShow': False,
-            'waterLineColor': (0, 255, 255),
-            'waterLineWidth': 3
-        }
-    })
-
-    CITY_MANGEMENT_MODEL = ("16", "016", "城管模型", 'cityMangement2', lambda device, gpuName: {
-        'device': device,
-        'labelnames': ["车辆", "垃圾", "商贩", "违停"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':5,'windowsize':29,'patchCnt':100},       
-        'postProcess':{
-            'function':dmpr_yolo_stdc,
-            'pars':{'carCls':0 ,'illCls':3,'scaleRatio':0.5,'border':80}
-            }, 
-        'models':[
-                {
-                    'weight':"../AIlib2/weights/cityMangement3/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":0.8,"1":0.5,"2":0.5,"3":0.5  } }
-                
-                },
-                {
-                    'weight':"../AIlib2/weights/cityMangement3/dmpr_%s.engine"% gpuName,###DMPR模型路径
-                    'par':{
-                        'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.3, 'dmprimg_size':640,
-                        'name':'dmpr'
-                            },
-                    'model':DMPRModel,     
-                    'name':'dmpr'
-                },
-                {
-                    'weight':"../AIlib2/weights/cityMangement3/stdc_360X640_%s_fp16.engine"% gpuName,###分割模型路径
-                    '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'        
-                    
-                } 
-            ],
-        '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
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 20,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'segLineShow': False,
-            'waterLineColor': (0, 255, 255),
-            'waterLineWidth': 2
-        }
-    })
-
-    DROWING_MODEL = ("17", "017", "人员落水模型", 'drowning', lambda device, gpuName: {
-        'device': device,
-        'labelnames': ["人头", "人", "船只"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-        'postProcess':{'function':mixDrowing_water_postprocess_N,
-                    'pars':{ }},
-        'models':
-            [
-                {
-                    'weight':"../AIlib2/weights/drowning/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  } },       
-                },
-                {
-                    'weight':'../AIlib2/weights/conf/drowning/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'        
-                }      
-            ],
-        '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    
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.25,
-            "classes": 9,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 20,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'waterLineColor': (0, 255, 255),
-            'segLineShow': False,
-            'waterLineWidth': 2
-        }
-    })
-
-    NOPARKING_MODEL = (
-        "18", "018", "城市违章模型", 'noParking', lambda device, gpuName: {
-            'device': device,
-            'labelnames': ["车辆", "违停"],
-            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-            'postProcess':{'function':mixNoParking_road_postprocess_N,
-                        'pars': { 'roundness': 0.3, 'cls': 9,  'laneArea': 10, 'laneAngleCha': 5 ,'RoadArea': 16000,'fitOrder':2, 'modelSize':(640,360)}
-                        } ,
-            'models':
-                [
-                    {
-                        'weight':"../AIlib2/weights/noParking/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  } },       
-                    },
-                    {
-                        'weight':'../AIlib2/weights/conf/noParking/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':4},###分割模型预处理参数
-                        'model':stdcModel,
-                        'name':'stdc'        
-                    }
-                ],
-            '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
-            'postFile': {
-                "name": "post_process",
-                "conf_thres": 0.25,
-                "iou_thres": 0.25,
-                "classes": 9,
-                "rainbows": COLOR
-            },
-            'txtFontSize': 20,
-            'digitFont': {
-                'line_thickness': 2,
-                'boxLine_thickness': 1,
-                'fontSize': 1.0,
-                'waterLineColor': (0, 255, 255),
-                'segLineShow': False,
-                'waterLineWidth': 2
-            }
-        }
-    )
-
-    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"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
-            'postProcess':{'function':default_mix,'pars':{ }},
-            'models':
-                [
-                    {
-                        'weight':"../AIlib2/weights/cityRoad/yolov5_%s_fp16.engine"% gpuName,###检测模型路径
-                        'name':'yolov5',
-                        'model':yolov5Model,
-                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.8,'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  } },       
-                    }
-                ],  
-            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.8,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 40,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'segLineShow': False,
-            'waterLineColor': (0, 255, 255),
-            'waterLineWidth': 3
-        }
-    })
-
-    POTHOLE_MODEL = ("23", "023", "坑槽检测模型", 'pothole', lambda device, gpuName: {   # 目前集成到另外的模型中去了 不单独使用
-        'device': device,
-        'labelnames': ["坑槽"],
-        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':3,'windowsize':29,'patchCnt':100},
-        'postProcess':{'function':default_mix,'pars':{ }},
-        'models':
-            [
-                {
-                    'weight':"../AIlib2/weights/pothole/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}},       
-                }
-            ],  
-        'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0]],###控制哪些检测类别显示、输出
-        'postFile': {
-            "name": "post_process",
-            "conf_thres": 0.25,
-            "iou_thres": 0.45,
-            "classes": 5,
-            "rainbows": COLOR
-        },
-        'txtFontSize': 40,
-        'digitFont': {
-            'line_thickness': 2,
-            'boxLine_thickness': 1,
-            'fontSize': 1.0,
-            'segLineShow': False,
-            'waterLineColor': (0, 255, 255),
-            'waterLineWidth': 3
-        },
-    })
-
-
-    @staticmethod
-    def checkCode(code):
-        for model in ModelType2:
-            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 BaiduModelTarget2(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_CONFIG2 = {
-    BaiduModelTarget2.VEHICLE_DETECTION.value[1]: BaiduModelTarget2.VEHICLE_DETECTION,
-    BaiduModelTarget2.HUMAN_DETECTION.value[1]: BaiduModelTarget2.HUMAN_DETECTION,
-    BaiduModelTarget2.PEOPLE_COUNTING.value[1]: BaiduModelTarget2.PEOPLE_COUNTING
-}
-
-EPIDEMIC_PREVENTION_CONFIG = {1: "行程码", 2: "健康码"}
-
-
-# 模型分析方式
-@unique
-class ModelMethodTypeEnum2(Enum):
-    # 方式一: 正常识别方式
-    NORMAL = 1
-
-    # 方式二: 追踪识别方式
-    TRACE = 2

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。

service/Dispatcher-raw.py

@@ -0,0 +1,507 @@
+# -*- 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

@@ -2,34 +2,44 @@
 import sys
 from pickle import dumps, loads
 from traceback import format_exc
-import time
 
 import cv2
 from loguru import logger
 
-from common.Constant import COLOR
-from enums.BaiduSdkEnum import VehicleEnum
 from enums.ExceptionEnum import ExceptionType
-from enums.ModelTypeEnum import ModelType, BAIDU_MODEL_TARGET_CONFIG
+from enums.ModelTypeEnum import ModelType
 from exception.CustomerException import ServiceException
-from util.ImgBaiduSdk import AipBodyAnalysisClient, AipImageClassifyClient
-from util.PlotsUtils import get_label_arrays, get_label_array_dict
+from util.PlotsUtils import get_label_arrays
 from util.TorchUtils import select_device
 
 sys.path.extend(['..', '../AIlib2'])
-from AI import AI_process, AI_process_forest, get_postProcess_para, ocr_process, AI_process_N, AI_process_C,AI_process_Ocr,AI_process_Crowd
+from AI import AI_process
 from stdc import stdcModel
-from segutils.segmodel import SegModel
 from models.experimental import attempt_load
-from obbUtils.shipUtils import OBB_infer
-from obbUtils.load_obb_model import load_model_decoder_OBB
-import torch
 import tensorrt as trt
-from utilsK.jkmUtils import pre_process, post_process, get_return_data
 from DMPR import DMPRModel
-from segment_anything import SamPredictor, sam_model_registry
 FONT_PATH = "../AIlib2/conf/platech.ttf"
 
+def get_label_arraylist(*args):
+    width, height, names, rainbows = args
+    # line = int(round(0.002 * (height + width) / 2) + 1)
+    line = max(1, int(round(width / 1920 * 3)))
+    label = ' 0.95'
+    tf = max(line - 1, 1)
+    fontScale = line * 0.33
+    text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
+    # fontsize = int(width / 1920 * 40)
+    numFontSize = float(format(width / 1920 * 1.1, '.1f'))
+    digitFont = {'line_thickness': line,
+                 'boxLine_thickness': line,
+                 'fontSize': numFontSize,
+                 'waterLineColor': (0, 255, 255),
+                 'segLineShow': False,
+                 'waterLineWidth': line,
+                 'wordSize': text_height,
+                 'label_location': 'leftTop'}
+    label_arraylist = get_label_arrays(names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
+    return digitFont, label_arraylist, (line, text_width, text_height, fontScale, tf)
 
 # 河道模型、河道检测模型、交通模型、人员落水模型、城市违章公共模型
 class OneModel:
@@ -37,7 +47,6 @@ class OneModel:
 
     def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
         try:
-            start = time.time()
             logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
                         requestId)
             par = modeType.value[4](str(device), gpu_name)
@@ -70,11 +79,10 @@ class OneModel:
                 'ovlap_thres_crossCategory': postFile.get("ovlap_thres_crossCategory"),
                 'iou_thres': postFile["iou_thres"],
                 # 对高速模型进行过滤
+                'allowedList': par['allowedList'] if modeType.value[0] == '3' else [],
                 'segRegionCnt': par['segRegionCnt'],
                 'trtFlag_det': par['trtFlag_det'],
-                'trtFlag_seg': par['trtFlag_seg'],
-                'score_byClass':par['score_byClass'] if 'score_byClass' in par.keys() else None,
-                'fiterList': par['fiterList'] if 'fiterList' in par.keys() else []
+                'trtFlag_seg': par['trtFlag_seg']
             }
             model_param = {
                 "model": model,
@@ -89,49 +97,15 @@ class OneModel:
             logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
             raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
                                    ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-        logger.info("模型初始化时间：{}, requestId:{}", time.time() - start, requestId)
-# 纯分类模型
-class cityManagementModel:
-    __slots__ = "model_conf"
-
-    def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
-        try:
-            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
-                        requestId)
-            par = modeType.value[4](str(device), gpu_name)
-            postProcess = par['postProcess']
-            names = par['labelnames']
-            postFile = par['postFile']
-            rainbows = postFile["rainbows"]
-            modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par'])  for  modelPar in par['models'] ]
-            model_param = {
-                "modelList": modelList,
-                "postProcess": postProcess,
-                "score_byClass":par['score_byClass'] if 'score_byClass' in par.keys() else None,
-                "fiterList":par['fiterList'] if 'fiterList' in par.keys() else [],
-            }
-            self.model_conf = (modeType, model_param, allowedList, names, rainbows)
-        except Exception:
-            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
-            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-def detSeg_demo2(args):
-    model_conf, frame, request_id = args
-    modelList, postProcess,score_byClass,fiterList = (
-        model_conf[1]['modelList'], model_conf[1]['postProcess'],model_conf[1]['score_byClass'], model_conf[1]['fiterList'])
-    try:
-        result =   [[ None, None, AI_process_N([frame], modelList, postProcess,score_byClass,fiterList)[0] ]  ]   # 为了让返回值适配统一的接口而写的shi
-        return result
-    except ServiceException as s:
-        raise s
-    except Exception:
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
 
 def model_process(args):
     model_conf, frame, request_id = args
     model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
+    # modeType, model_param, allowedList, names, rainbows = model_conf
+    #   segmodel, names, label_arraylist, rainbows, objectPar, font, segPar, mode, postPar, requestId = args
+    # model_param['digitFont'] = digitFont
+    # model_param['label_arraylist'] = label_arraylist
+    # model_param['font_config'] = font_config
     try:
         return AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
                           rainbows, objectPar=model_param['objectPar'], font=model_param['digitFont'],
@@ -146,343 +120,6 @@ def model_process(args):
         raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
                                ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
 
-
-# 森林模型、车辆模型、行人模型、烟火模型、 钓鱼模型、航道模型、乡村模型、城管模型公共模型
-class TwoModel:
-    __slots__ = "model_conf"
-
-    def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
-                 env=None):
-        s = time.time()
-        try:
-            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
-                        requestId)
-            par = modeType.value[4](str(device1), gpu_name)
-            device = select_device(par.get('device'))
-            names = par['labelnames']
-            half = 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 modeType == ModelType.CITY_FIREAREA_MODEL:
-                sam = sam_model_registry[par['sam_type']](checkpoint=par['Samweights'])
-                sam.to(device=device)
-                segmodel = SamPredictor(sam)
-            else:
-                segmodel = None
-
-            postFile = par['postFile']
-            conf_thres = postFile["conf_thres"]
-            iou_thres = postFile["iou_thres"]
-            rainbows = postFile["rainbows"]
-            otc = postFile.get("ovlap_thres_crossCategory")
-            model_param = {
-                "model": model,
-                "segmodel": segmodel,
-                "half": half,
-                "device": device,
-                "conf_thres": conf_thres,
-                "iou_thres": iou_thres,
-                "trtFlag_det": par['trtFlag_det'],
-                "otc": otc,
-                "ksize":par['ksize'] if 'ksize' in par.keys() else None,
-                "score_byClass": par['score_byClass'] if 'score_byClass' in par.keys() else None,
-                "fiterList": par['fiterList'] if 'fiterList' in par.keys() else []
-            }
-            self.model_conf = (modeType, model_param, allowedList, names, rainbows)
-        except Exception:
-            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
-            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-        logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
-def forest_process(args):
-    model_conf, frame, request_id = args
-    model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
-    try:
-        return AI_process_forest([frame], model_param['model'], model_param['segmodel'], names,
-                                 model_param['label_arraylist'], rainbows, model_param['half'], model_param['device'],
-                                 model_param['conf_thres'], model_param['iou_thres'],font=model_param['digitFont'],
-                                 trtFlag_det=model_param['trtFlag_det'], SecNms=model_param['otc'],ksize = model_param['ksize'],
-                                 score_byClass=model_param['score_byClass'],fiterList=model_param['fiterList'])
-    except ServiceException as s:
-        raise s
-    except Exception:
-        # self.num += 1
-        # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-class MultiModel:
-    __slots__ = "model_conf"
-
-    def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
-                 env=None):
-        s = time.time()
-        try:
-            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
-                        requestId)
-            par = modeType.value[4](str(device1), gpu_name)
-            postProcess = par['postProcess']
-            names = par['labelnames']
-            postFile = par['postFile']
-            rainbows = postFile["rainbows"]
-            modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par'])  for  modelPar in par['models'] ]
-            model_param = {
-                "modelList": modelList,
-                "postProcess": postProcess,
-                "score_byClass": par['score_byClass'] if 'score_byClass' in par.keys() else None,
-                "fiterList": par['fiterList'] if 'fiterList' in par.keys() else []
-            }
-            self.model_conf = (modeType, model_param, allowedList, names, rainbows)
-        except Exception:
-            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
-            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-        logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
-def channel2_process(args):
-    model_conf, frame, request_id = args
-    modelList, postProcess,score_byClass,fiterList = (
-        model_conf[1]['modelList'], model_conf[1]['postProcess'],model_conf[1]['score_byClass'], model_conf[1]['fiterList'])
-    try:
-        start = time.time()
-        result = [[None, None, AI_process_C([frame], modelList, postProcess,score_byClass,fiterList)[0]]]   # 为了让返回值适配统一的接口而写的shi
-        # print("AI_process_C use time = {}".format(time.time()-start))
-        return result
-    except ServiceException as s:
-        raise s
-    except Exception:
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-def get_label_arraylist(*args):
-    width, height, names, rainbows = args
-    # line = int(round(0.002 * (height + width) / 2) + 1)
-    line = max(1, int(round(width / 1920 * 3)))
-    label = ' 0.95'
-    tf = max(line - 1, 1)
-    fontScale = line * 0.33
-    text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
-    # fontsize = int(width / 1920 * 40)
-    numFontSize = float(format(width / 1920 * 1.1, '.1f'))
-    digitFont = {'line_thickness': line,
-                 'boxLine_thickness': line,
-                 'fontSize': numFontSize,
-                 'waterLineColor': (0, 255, 255),
-                 'segLineShow': False,
-                 'waterLineWidth': line,
-                 'wordSize': text_height,
-                 'label_location': 'leftTop'}
-    label_arraylist = get_label_arrays(names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
-    return digitFont, label_arraylist, (line, text_width, text_height, fontScale, tf)
-# 船只模型
-class ShipModel:
-    __slots__ = "model_conf"
-
-    def __init__(self, device1, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
-                 env=None):
-        s = time.time()
-        try:
-            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
-                        requestId)
-            par = modeType.value[4](str(device1), gpu_name)
-            model, decoder2 = load_model_decoder_OBB(par)
-            par['decoder'] = decoder2
-            names = par['labelnames']
-            rainbows = par['postFile']["rainbows"]
-            model_param = {
-                "model": model,
-                "par": par
-            }
-            self.model_conf = (modeType, model_param, allowedList, names, rainbows)
-        except Exception:
-            logger.exception("模型加载异常：{}, requestId:{}", format_exc(), requestId)
-            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-        logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
-def obb_process(args):
-    model_conf, frame, request_id = args
-    model_param = model_conf[1]
-    # font_config, frame, names, label_arrays, rainbows, model, par, requestId = args
-    try:
-        return OBB_infer(model_param["model"], frame, model_param["par"])
-    except ServiceException as s:
-        raise s
-    except Exception:
-        # self.num += 1
-        # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-# 车牌分割模型、健康码、行程码分割模型
-class IMModel:
-    __slots__ = "model_conf"
-
-    def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
-                 env=None):
-        try:
-            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
-                        requestId)
-            img_type = 'code'
-            if ModelType.PLATE_MODEL == modeType:
-                img_type = 'plate'
-            par = {
-                'code': {'weights': '../weights/pth/AIlib2/jkm/health_yolov5s_v3.jit', 'img_type': 'code', 'nc': 10},
-                'plate': {'weights': '../weights/pth/AIlib2/jkm/plate_yolov5s_v3.jit', 'img_type': 'plate', 'nc': 1},
-                'conf_thres': 0.4,
-                'iou_thres': 0.45,
-                'device': 'cuda:%s' % device,
-                'plate_dilate': (0.5, 0.3)
-            }
-
-            new_device = torch.device(par['device'])
-            model = torch.jit.load(par[img_type]['weights'])
-            logger.info("########################加载 jit 模型成功 成功 ########################, requestId:{}",
-                        requestId)
-            self.model_conf = (modeType, allowedList, new_device, model, par, img_type)
-        except Exception:
-            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
-            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-
-def im_process(args):
-    frame, device, model, par, img_type, requestId = args
-    try:
-        img, padInfos = pre_process(frame, device)
-        pred = model(img)
-        boxes = post_process(pred, padInfos, device, conf_thres=par['conf_thres'],
-                             iou_thres=par['iou_thres'], nc=par[img_type]['nc'])  # 后处理
-        dataBack = get_return_data(frame, boxes, modelType=img_type, plate_dilate=par['plate_dilate'])
-        print('-------line351----:',dataBack)
-        return dataBack
-    except ServiceException as s:
-        raise s
-    except Exception:
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
-def immulti_process(args):
-    model_conf, frame, requestId = args
-    device, modelList, detpar = model_conf[1], model_conf[2], model_conf[3]
-    try:
-        # new_device = torch.device(device)
-        # img, padInfos = pre_process(frame, new_device)
-        # pred = model(img)
-        # boxes = post_process(pred, padInfos, device, conf_thres=pardet['conf_thres'],
-        #                      iou_thres=pardet['iou_thres'], nc=pardet['nc'])  # 后处理
-        return AI_process_Ocr([frame], modelList, device, detpar)
-    except ServiceException as s:
-        raise s
-    except Exception:
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
-class CARPLATEModel:
-    __slots__ = "model_conf"
-    def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
-                 env=None):
-        try:
-            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
-                        requestId)
-            par = modeType.value[4](str(device), gpu_name)
-            modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par'])  for  modelPar in par['models'] ]
-            detpar = par['models'][0]['par']
-            # new_device = torch.device(par['device'])
-            # modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par'])  for  modelPar in par['models'] ]
-            self.model_conf = (modeType, device, modelList, detpar, par['rainbows'])
-        except Exception:
-            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
-            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-
-class DENSECROWDCOUNTModel:
-    __slots__ = "model_conf"
-
-    def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
-        try:
-            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
-                        requestId)
-            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 = [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],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-
-def cc_process(args):
-    model_conf, frame, requestId = args
-    device, model, postPar = model_conf[1], model_conf[2], model_conf[3]
-    try:
-        return AI_process_Crowd([frame], model, device, postPar)
-    except ServiceException as s:
-        raise s
-    except Exception:
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), requestId)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
-
-# 百度AI图片识别模型
-class BaiduAiImageModel:
-    __slots__ = "model_conf"
-
-    def __init__(self, device=None, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
-                 env=None):
-        try:
-            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
-                        requestId)
-            # 人体检测与属性识别、 人流量统计客户端
-            aipBodyAnalysisClient = AipBodyAnalysisClient(base_dir, env)
-            # 车辆检测检测客户端
-            aipImageClassifyClient = AipImageClassifyClient(base_dir, env)
-            rainbows = COLOR
-            vehicle_names = [VehicleEnum.CAR.value[1], VehicleEnum.TRICYCLE.value[1], VehicleEnum.MOTORBIKE.value[1],
-                             VehicleEnum.CARPLATE.value[1], VehicleEnum.TRUCK.value[1], VehicleEnum.BUS.value[1]]
-            person_names = ['人']
-            self.model_conf = (modeType, aipImageClassifyClient, aipBodyAnalysisClient, allowedList, rainbows,
-                               vehicle_names, person_names, requestId)
-        except Exception:
-            logger.exception("模型加载异常：{}, requestId:{}", format_exc(), requestId)
-            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-
-
-def get_baidu_label_arraylist(*args):
-    width, height, vehicle_names, person_names, rainbows = args
-    # line = int(round(0.002 * (height + width) / 2) + 1)
-    line = max(1, int(round(width / 1920 * 3) + 1))
-    label = ' 0.97'
-    tf = max(line, 1)
-    fontScale = line * 0.33
-    text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
-    vehicle_label_arrays = get_label_arrays(vehicle_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
-    person_label_arrays = get_label_arrays(person_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
-    font_config = (line, text_width, text_height, fontScale, tf)
-    return vehicle_label_arrays, person_label_arrays, font_config
-
-
-def baidu_process(args):
-    target, url, aipImageClassifyClient, aipBodyAnalysisClient, request_id = args
-    try:
-        # [target, url, aipImageClassifyClient, aipBodyAnalysisClient, requestId]
-        baiduEnum = BAIDU_MODEL_TARGET_CONFIG.get(target)
-        if baiduEnum is None:
-            raise ServiceException(ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[0],
-                                   ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[1]
-                                   + " target: " + target)
-        return baiduEnum.value[2](aipImageClassifyClient, aipBodyAnalysisClient, url, request_id)
-    except ServiceException as s:
-        raise s
-    except Exception:
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
-
 def one_label(width, height, model_conf):
     # modeType, model_param, allowedList, names, rainbows = model_conf
     names = model_conf[3]
@@ -493,288 +130,13 @@ def one_label(width, height, model_conf):
     model_param['label_arraylist'] = label_arraylist
     model_param['font_config'] = font_config
 
-def dynamics_label(width, height, model_conf):
-    # modeType, model_param, allowedList, names, rainbows = model_conf
-    names = model_conf[3]
-    rainbows = model_conf[4]
-    model_param = model_conf[1]
-    digitFont, label_arraylist, font_config = get_label_arraylist(width, height, names, rainbows)
-    line = max(1, int(round(width / 1920 * 3)))
-    label = ' 0.95'
-    tf = max(line - 1, 1)
-    fontScale = line * 0.33
-    _, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
-    label_dict = get_label_array_dict(rainbows, fontSize=text_height, fontPath=FONT_PATH)
-    model_param['digitFont'] = digitFont
-    model_param['label_arraylist'] = label_arraylist
-    model_param['font_config'] = font_config
-    model_param['label_dict'] = label_dict
-def baidu_label(width, height, model_conf):
-    # modeType, aipImageClassifyClient, aipBodyAnalysisClient, allowedList, rainbows,
-    # vehicle_names, person_names, requestId
-    vehicle_names = model_conf[5]
-    person_names = model_conf[6]
-    rainbows = model_conf[4]
-    vehicle_label_arrays, person_label_arrays, font_config = get_baidu_label_arraylist(width, height, vehicle_names,
-                                                                                       person_names, rainbows)
-    return vehicle_label_arrays, person_label_arrays, font_config
-
 
 MODEL_CONFIG = {
-    # 加载河道模型
-    ModelType.WATER_SURFACE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.WATER_SURFACE_MODEL, t, z, h),
-        ModelType.WATER_SURFACE_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 加载森林模型
-    # ModelType.FOREST_FARM_MODEL.value[1]: (
-        # lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.FOREST_FARM_MODEL, t, z, h),
-        # ModelType.FOREST_FARM_MODEL,
-        # lambda x, y, z: one_label(x, y, z),
-        # lambda x: forest_process(x)
-    # ),
-    ModelType.FOREST_FARM_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FOREST_FARM_MODEL, t, z, h),
-        ModelType.FOREST_FARM_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: detSeg_demo2(x)
-    ),
-
-    # 加载交通模型
-    ModelType.TRAFFIC_FARM_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_FARM_MODEL, t, z, h),
-        ModelType.TRAFFIC_FARM_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 加载防疫模型
-    ModelType.EPIDEMIC_PREVENTION_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType.EPIDEMIC_PREVENTION_MODEL, t, z, h),
-        ModelType.EPIDEMIC_PREVENTION_MODEL,
-        None,
-        lambda x: im_process(x)),
-    # 加载车牌模型
-    ModelType.PLATE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType.PLATE_MODEL, t, z, h),
-        ModelType.PLATE_MODEL,
-        None,
-        lambda x: im_process(x)),
-    # 加载车辆模型
-    ModelType.VEHICLE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.VEHICLE_MODEL, t, z, h),
-        ModelType.VEHICLE_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: forest_process(x)
-    ),
-    # 加载行人模型
-    ModelType.PEDESTRIAN_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.PEDESTRIAN_MODEL, t, z, h),
-        ModelType.PEDESTRIAN_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: forest_process(x)),
-    # 加载烟火模型
-    ModelType.SMOGFIRE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.SMOGFIRE_MODEL, t, z, h),
-        ModelType.SMOGFIRE_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: forest_process(x)),
-    # 加载钓鱼游泳模型
-    ModelType.ANGLERSWIMMER_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.ANGLERSWIMMER_MODEL, t, z, h),
-        ModelType.ANGLERSWIMMER_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: forest_process(x)),
-    # 加载乡村模型
-    ModelType.COUNTRYROAD_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.COUNTRYROAD_MODEL, t, z, h),
-        ModelType.COUNTRYROAD_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: forest_process(x)),
-    # 加载船只模型
-    ModelType.SHIP_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: ShipModel(x, y, r, ModelType.SHIP_MODEL, t, z, h),
-        ModelType.SHIP_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: obb_process(x)),
-    # 百度AI图片识别模型
-    ModelType.BAIDU_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: BaiduAiImageModel(x, y, r, ModelType.BAIDU_MODEL, t, z, h),
-        ModelType.BAIDU_MODEL,
-        lambda x, y, z: baidu_label(x, y, z),
-        lambda x: baidu_process(x)),
-    # 航道模型
-    ModelType.CHANNEL_EMERGENCY_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CHANNEL_EMERGENCY_MODEL, t, z, h),
-        ModelType.CHANNEL_EMERGENCY_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: forest_process(x)),
     # 河道检测模型
     ModelType.RIVER2_MODEL.value[1]: (
         lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.RIVER2_MODEL, t, z, h),
         ModelType.RIVER2_MODEL,
         lambda x, y, z: one_label(x, y, z),
         lambda x: model_process(x)),
-    # 城管模型
-    ModelType.CITY_MANGEMENT_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_MANGEMENT_MODEL, t, z, h),
-        ModelType.CITY_MANGEMENT_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: detSeg_demo2(x)
-    ),
-    # 人员落水模型
-    ModelType.DROWING_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.DROWING_MODEL, t, z, h),
-        ModelType.DROWING_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 城市违章模型
-    ModelType.NOPARKING_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.NOPARKING_MODEL, t, z, h),
-        ModelType.NOPARKING_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 车辆违停模型
-    ModelType.ILLPARKING_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.ILLPARKING_MODEL, t, z, h),
-        ModelType.ILLPARKING_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 城市公路模型
-    ModelType.CITYROAD_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CITYROAD_MODEL, t, z, h),
-        ModelType.CITYROAD_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: forest_process(x)),
-    # 加载坑槽模型
-    ModelType.POTHOLE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.POTHOLE_MODEL, t, z, h),
-        ModelType.POTHOLE_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: forest_process(x)
-    ),
-    # 加载船只综合检测模型
-    ModelType.CHANNEL2_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: MultiModel(x, y, r, ModelType.CHANNEL2_MODEL, t, z, h),
-        ModelType.CHANNEL2_MODEL,
-        lambda x, y, z: dynamics_label(x, y, z),
-        lambda x: channel2_process(x)
-    ),
-    # 河道检测模型
-    ModelType.RIVERT_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.RIVERT_MODEL, t, z, h),
-        ModelType.RIVERT_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)),
-    # 加载森林人群模型
-    ModelType.FORESTCROWD_FARM_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FORESTCROWD_FARM_MODEL, t, z, h),
-        ModelType.FORESTCROWD_FARM_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: detSeg_demo2(x)
-    ),
-    # 加载交通模型
-    ModelType.TRAFFICFORDSJ_FARM_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFICFORDSJ_FARM_MODEL, t, z, h),
-        ModelType.TRAFFICFORDSJ_FARM_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 加载智慧工地模型
-    ModelType.SMARTSITE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.SMARTSITE_MODEL, t, z, h),
-        ModelType.SMARTSITE_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: detSeg_demo2(x)
-    ),
 
-    # 加载垃圾模型
-    ModelType.RUBBISH_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.RUBBISH_MODEL, t, z, h),
-        ModelType.RUBBISH_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: detSeg_demo2(x)
-    ),
-
-    # 加载烟花模型    
-    ModelType.FIREWORK_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.FIREWORK_MODEL, t, z, h),
-        ModelType.FIREWORK_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: detSeg_demo2(x)
-    ),
-    # 加载高速公路抛撒物模型
-    ModelType.TRAFFIC_SPILL_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_SPILL_MODEL, t, z, h),
-        ModelType.TRAFFIC_SPILL_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 加载高速公路危化品模型
-    ModelType.TRAFFIC_CTHC_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_CTHC_MODEL, t, z, h),
-        ModelType.TRAFFIC_CTHC_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 加载光伏板异常检测模型
-    ModelType.TRAFFIC_PANNEL_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.TRAFFIC_PANNEL_MODEL, t, z, h),
-        ModelType.TRAFFIC_PANNEL_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: detSeg_demo2(x)
-    ),
-    # 加载自研车牌检测模型
-    ModelType.CITY_CARPLATE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: CARPLATEModel(x, y, r, ModelType.CITY_CARPLATE_MODEL, t, z, h),
-        ModelType.CITY_CARPLATE_MODEL,
-        None,
-        lambda x: immulti_process(x)
-    ),
-    # 加载红外行人检测模型
-    ModelType.CITY_INFRAREDPERSON_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_INFRAREDPERSON_MODEL, t, z, h),
-        ModelType.CITY_INFRAREDPERSON_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: detSeg_demo2(x)
-    ),
-    # 加载夜间烟火检测模型
-    ModelType.CITY_NIGHTFIRESMOKE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_NIGHTFIRESMOKE_MODEL, t, z, h),
-        ModelType.CITY_NIGHTFIRESMOKE_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: detSeg_demo2(x)
-    ),
-    # 加载密集人群计数检测模型
-    ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: DENSECROWDCOUNTModel(x, y, r, ModelType.CITY_DENSECROWDCOUNT_MODEL, t, z, h),
-        ModelType.CITY_DENSECROWDCOUNT_MODEL,
-        None,
-        lambda x: cc_process(x)
-    ),
-    # 加载建筑物下行人检测模型
-    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: DENSECROWDCOUNTModel(x, y, r, ModelType.CITY_UNDERBUILDCOUNT_MODEL, t, z, h),
-        ModelType.CITY_UNDERBUILDCOUNT_MODEL,
-        None,
-        lambda x: cc_process(x)
-    ),
-    # 加载火焰面积模型
-    ModelType.CITY_FIREAREA_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: TwoModel(x, y, r, ModelType.CITY_FIREAREA_MODEL, t, z, h),
-        ModelType.CITY_FIREAREA_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: forest_process(x)
-    ),
-    # 加载安防模型
-    ModelType.CITY_SECURITY_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_SECURITY_MODEL, t, z, h),
-        ModelType.CITY_SECURITY_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: detSeg_demo2(x)
-    ),   
 }

util/ModelUtils2.py

@@ -1,442 +0,0 @@
-# -*- coding: utf-8 -*-
-import sys
-from json import dumps, loads
-from traceback import format_exc
-
-import cv2
-from loguru import logger
-
-from common.Constant import COLOR
-from enums.BaiduSdkEnum import VehicleEnum
-from enums.ExceptionEnum import ExceptionType
-from enums.ModelTypeEnum2 import ModelType2, BAIDU_MODEL_TARGET_CONFIG2
-from exception.CustomerException import ServiceException
-from util.ImgBaiduSdk import AipBodyAnalysisClient, AipImageClassifyClient
-from util.PlotsUtils import get_label_arrays
-from util.TorchUtils import select_device
-import time
-import torch
-import tensorrt as trt
-
-sys.path.extend(['..', '../AIlib2'])
-from AI import AI_process, get_postProcess_para, get_postProcess_para_dic, AI_det_track, AI_det_track_batch, AI_det_track_batch_N
-from stdc import stdcModel
-from utilsK.jkmUtils import pre_process, post_process, get_return_data
-from obbUtils.shipUtils import OBB_infer, OBB_tracker, draw_obb, OBB_tracker_batch
-from obbUtils.load_obb_model import load_model_decoder_OBB
-from trackUtils.sort import Sort
-from trackUtils.sort_obb import OBB_Sort
-from DMPR import DMPRModel
-
-FONT_PATH = "../AIlib2/conf/platech.ttf"
-
-
-class Model:
-    __slots__ = "model_conf"
-
-    def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
-        try:
-            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
-                        requestId)
-            par = modeType.value[4](str(device), gpu_name)
-            trackPar = par['trackPar']
-            names = par['labelnames']
-            detPostPar = par['postFile']
-            rainbows = detPostPar["rainbows"]
-            #第一步加载模型
-            modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par'])  for  modelPar in par['models'] ]
-            #第二步准备跟踪参数
-            trackPar=par['trackPar']
-            sort_tracker = Sort(max_age=trackPar['sort_max_age'],
-                            min_hits=trackPar['sort_min_hits'],
-                            iou_threshold=trackPar['sort_iou_thresh'])
-            postProcess = par['postProcess']
-            model_param = {
-                "modelList": modelList,
-                "postProcess": postProcess,
-                "sort_tracker": sort_tracker,
-                "trackPar": trackPar,
-            }
-            self.model_conf = (modeType, model_param, allowedList, names, rainbows)
-        except Exception:
-            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
-            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-
-def get_label_arraylist(*args):
-    width, height, names, rainbows = args
-    # line = int(round(0.002 * (height + width) / 2) + 1)
-    line = max(1, int(round(width / 1920 * 3)))
-    tf = max(line, 1)
-    fontScale = line * 0.33
-    text_width, text_height = cv2.getTextSize(' 0.95', 0, fontScale=fontScale, thickness=tf)[0]
-    label_arraylist = get_label_arrays(names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
-    return label_arraylist, (line, text_width, text_height, fontScale, tf)
-
-
-"""
-输入：
-    imgarray_list--图像列表
-    iframe_list -- 帧号列表
-    modelPar--模型参数,字典,modelPar={'det_Model':,'seg_Model':}
-    processPar--字典，存放检测相关参数，'half', 'device', 'conf_thres', 'iou_thres','trtFlag_det'
-    sort_tracker--对象，初始化的跟踪对象。为了保持一致，即使是单帧也要有。
-    trackPar--跟踪参数，关键字包括：det_cnt，windowsize
-    segPar--None,分割模型相关参数。如果用不到，则为None
-输入：retResults,timeInfos
-    retResults：list
-        retResults[0]--imgarray_list
-        retResults[1]--所有结果用numpy格式，所有的检测结果，包括8类，每列分别是x1, y1, x2, y2, conf, detclass,iframe,trackId
-        retResults[2]--所有结果用list表示,其中每一个元素为一个list，表示每一帧的检测结果，每一个结果是由多个list构成，每个list表示一个框，格式为[ cls , x0 ,y0  ,x1  ,y1  ,conf,ifrmae,trackId ]，如 retResults[2][j][k]表示第j帧的第k个框。
-"""
-
-
-def model_process(args):
-    # (modeType, model_param, allowedList, names, rainbows)
-    imgarray_list, iframe_list, model_param, request_id = args
-    try:
-        return AI_det_track_batch_N(imgarray_list, iframe_list, 
-                                    model_param['modelList'], 
-                                    model_param['postProcess'], 
-                                    model_param['sort_tracker'], 
-                                    model_param['trackPar'])
-    except ServiceException as s:
-        raise s
-    except Exception:
-        # self.num += 1
-        # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
-        logger.error("算法模型分析异常: {}, requestId: {}", format_exc(), request_id)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
-
-# 船只模型
-class ShipModel:
-    __slots__ = "model_conf"
-
-    def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
-        s = time.time()
-        try:
-            logger.info("########################加载船只模型########################, requestId:{}", requestId)
-            par = modeType.value[4](str(device), gpu_name)
-            obbModelPar = par['obbModelPar']
-            model, decoder2 = load_model_decoder_OBB(obbModelPar)
-            obbModelPar['decoder'] = decoder2
-            names = par['labelnames']
-            rainbows = par['postFile']["rainbows"]
-            trackPar = par['trackPar']
-            sort_tracker = OBB_Sort(max_age=trackPar['sort_max_age'], min_hits=trackPar['sort_min_hits'],
-                                    iou_threshold=trackPar['sort_iou_thresh'])
-            modelPar = {'obbmodel': model}
-            segPar = None
-            model_param = {
-                "modelPar": modelPar,
-                "obbModelPar": obbModelPar,
-                "sort_tracker": sort_tracker,
-                "trackPar": trackPar,
-                "segPar": segPar
-            }
-            self.model_conf = (modeType, model_param, allowedList, names, rainbows)
-        except Exception:
-            logger.exception("模型加载异常：{}, requestId:{}", format_exc(), requestId)
-            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-        logger.info("模型初始化时间：{}, requestId:{}", time.time() - s, requestId)
-
-
-def obb_process(args):
-    imgarray_list, iframe_list, model_param, request_id = args
-    try:
-        return OBB_tracker_batch(imgarray_list, iframe_list, model_param['modelPar'], model_param['obbModelPar'],
-                                 model_param['sort_tracker'], model_param['trackPar'], model_param['segPar'])
-    except ServiceException as s:
-        raise s
-    except Exception:
-        # self.num += 1
-        # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
-
-# 车牌分割模型、健康码、行程码分割模型
-class IMModel:
-    __slots__ = "model_conf"
-
-    def __init__(self, device, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None, env=None):
-        try:
-            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
-                        requestId)
-            img_type = 'code'
-            if ModelType2.PLATE_MODEL == modeType:
-                img_type = 'plate'
-            par = {
-                'code': {'weights': '../AIlib2/weights/conf/jkm/health_yolov5s_v3.jit', 'img_type': 'code', 'nc': 10},
-                'plate': {'weights': '../AIlib2/weights/conf/jkm/plate_yolov5s_v3.jit', 'img_type': 'plate', 'nc': 1},
-                'conf_thres': 0.4,
-                'iou_thres': 0.45,
-                'device': 'cuda:%s' % device,
-                'plate_dilate': (0.5, 0.3)
-            }
-            new_device = torch.device(par['device'])
-            model = torch.jit.load(par[img_type]['weights'])
-            model_param = {
-                "device": new_device,
-                "model": model,
-                "par": par,
-                "img_type": img_type
-            }
-            self.model_conf = (modeType, model_param, allowedList)
-        except Exception:
-            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
-            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-
-
-def im_process(args):
-    model_param, frame, request_id = args
-    device, par, img_type = model_param['device'], model_param['par'], model_param['img_type']
-    try:
-        img, padInfos = pre_process(frame, device)
-        pred = model_param['model'](img)
-        boxes = post_process(pred, padInfos, device, conf_thres=par['conf_thres'],
-                             iou_thres=par['iou_thres'], nc=par[img_type]['nc'])  # 后处理
-        dataBack = get_return_data(frame, boxes, modelType=img_type, plate_dilate=par['plate_dilate'])
-        return dataBack
-    except ServiceException as s:
-        raise s
-    except Exception:
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
-
-# 百度AI图片识别模型
-class BaiduAiImageModel:
-    __slots__ = "model_conf"
-
-    def __init__(self, device=None, allowedList=None, requestId=None, modeType=None, gpu_name=None, base_dir=None,
-                 env=None):
-        try:
-            logger.info("########################加载{}########################, requestId:{}", modeType.value[2],
-                        requestId)
-            aipBodyAnalysisClient = AipBodyAnalysisClient(base_dir, env)
-            aipImageClassifyClient = AipImageClassifyClient(base_dir, env)
-            rainbows = COLOR
-            vehicle_names = [VehicleEnum.CAR.value[1], VehicleEnum.TRICYCLE.value[1], VehicleEnum.MOTORBIKE.value[1],
-                             VehicleEnum.CARPLATE.value[1], VehicleEnum.TRUCK.value[1], VehicleEnum.BUS.value[1]]
-            person_names = ['人']
-            model_param = {
-                "vehicle_client": aipImageClassifyClient,
-                "person_client": aipBodyAnalysisClient,
-            }
-            self.model_conf = (modeType, model_param, allowedList, (vehicle_names, person_names), rainbows)
-        except Exception:
-            logger.exception("模型加载异常：{}, requestId:{}", format_exc(), requestId)
-            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
-                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
-
-
-def baidu_process(args):
-    model_param, target, url, request_id = args
-    try:
-        baiduEnum = BAIDU_MODEL_TARGET_CONFIG2.get(target)
-        if baiduEnum is None:
-            raise ServiceException(ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[0],
-                                   ExceptionType.DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED.value[1]
-                                   + " target: " + target)
-        return baiduEnum.value[2](model_param['vehicle_client'], model_param['person_client'], url, request_id)
-    except ServiceException as s:
-        raise s
-    except Exception:
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
-
-def get_baidu_label_arraylist(*args):
-    width, height, vehicle_names, person_names, rainbows = args
-    # line = int(round(0.002 * (height + width) / 2) + 1)
-    line = max(1, int(round(width / 1920 * 3) + 1))
-    label = ' 0.97'
-    tf = max(line, 1)
-    fontScale = line * 0.33
-    text_width, text_height = cv2.getTextSize(label, 0, fontScale=fontScale, thickness=tf)[0]
-    vehicle_label_arrays = get_label_arrays(vehicle_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
-    person_label_arrays = get_label_arrays(person_names, rainbows, fontSize=text_height, fontPath=FONT_PATH)
-    font_config = (line, text_width, text_height, fontScale, tf)
-    return vehicle_label_arrays, person_label_arrays, font_config
-
-
-def one_label(width, height, model_config):
-    # (modeType, model_param, allowedList, names, rainbows)
-    names = model_config[3]
-    rainbows = model_config[4]
-    label_arraylist, font_config = get_label_arraylist(width, height, names, rainbows)
-    model_config[1]['label_arraylist'] = label_arraylist
-    model_config[1]['font_config'] = font_config
-
-
-def baidu_label(width, height, model_config):
-    # modeType, model_param, allowedList, (vehicle_names, person_names), rainbows
-    vehicle_names = model_config[3][0]
-    person_names = model_config[3][1]
-    rainbows = model_config[4]
-    vehicle_label_arrays, person_label_arrays, font_config = get_baidu_label_arraylist(width, height, vehicle_names,
-                                                                                       person_names, rainbows)
-    model_config[1]['vehicle_label_arrays'] = vehicle_label_arrays
-    model_config[1]['person_label_arrays'] = person_label_arrays
-    model_config[1]['font_config'] = font_config
-
-
-
-
-def model_process1(args):
-    imgarray_list, iframe_list, model_param, request_id = args
-    model_conf, frame, request_id = args
-    model_param, names, rainbows = model_conf[1], model_conf[3], model_conf[4]
-    # modeType, model_param, allowedList, names, rainbows = model_conf
-    #   segmodel, names, label_arraylist, rainbows, objectPar, font, segPar, mode, postPar, requestId = args
-    # model_param['digitFont'] = digitFont
-    # model_param['label_arraylist'] = label_arraylist
-    # model_param['font_config'] = font_config
-    try:
-        return AI_process([frame], model_param['model'], model_param['segmodel'], names, model_param['label_arraylist'],
-                          rainbows, objectPar=model_param['objectPar'], font=model_param['digitFont'],
-                          segPar=loads(dumps(model_param['segPar'])), mode=model_param['mode'],
-                          postPar=model_param['postPar'])
-    except ServiceException as s:
-        raise s
-    except Exception:
-        # self.num += 1
-        # cv2.imwrite('/home/th/tuo_heng/dev/img%s.jpg' % str(self.num), frame)
-        logger.error("算法模型分析异常：{}, requestId:{}", format_exc(), request_id)
-        raise ServiceException(ExceptionType.MODEL_ANALYSE_EXCEPTION.value[0],
-                               ExceptionType.MODEL_ANALYSE_EXCEPTION.value[1])
-
-
-MODEL_CONFIG2 = {
-    # 加载河道模型
-    ModelType2.WATER_SURFACE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.WATER_SURFACE_MODEL, t, z, h),
-        ModelType2.WATER_SURFACE_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 加载森林模型
-    ModelType2.FOREST_FARM_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.FOREST_FARM_MODEL, t, z, h),
-        ModelType2.FOREST_FARM_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 加载交通模型
-    ModelType2.TRAFFIC_FARM_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.TRAFFIC_FARM_MODEL, t, z, h),
-        ModelType2.TRAFFIC_FARM_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 加载防疫模型
-    ModelType2.EPIDEMIC_PREVENTION_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType2.EPIDEMIC_PREVENTION_MODEL, t, z, h),
-        ModelType2.EPIDEMIC_PREVENTION_MODEL,
-        None,
-        lambda x: im_process(x)),
-    # 加载车牌模型
-    ModelType2.PLATE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: IMModel(x, y, r, ModelType2.PLATE_MODEL, t, z, h),
-        ModelType2.PLATE_MODEL,
-        None,
-        lambda x: im_process(x)),
-    # 加载车辆模型
-    ModelType2.VEHICLE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.VEHICLE_MODEL, t, z, h),
-        ModelType2.VEHICLE_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 加载行人模型
-    ModelType2.PEDESTRIAN_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.PEDESTRIAN_MODEL, t, z, h),
-        ModelType2.PEDESTRIAN_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)),
-    # 加载烟火模型
-    ModelType2.SMOGFIRE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.SMOGFIRE_MODEL, t, z, h),
-        ModelType2.SMOGFIRE_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)),
-    # 加载钓鱼游泳模型
-    ModelType2.ANGLERSWIMMER_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.ANGLERSWIMMER_MODEL, t, z, h),
-        ModelType2.ANGLERSWIMMER_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)),
-    # 加载乡村模型
-    ModelType2.COUNTRYROAD_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.COUNTRYROAD_MODEL, t, z, h),
-        ModelType2.COUNTRYROAD_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)),
-    # 加载船只模型
-    ModelType2.SHIP_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: ShipModel(x, y, r, ModelType2.SHIP_MODEL, t, z, h),
-        ModelType2.SHIP_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: obb_process(x)),
-    # 百度AI图片识别模型
-    ModelType2.BAIDU_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: BaiduAiImageModel(x, y, r, ModelType2.BAIDU_MODEL, t, z, h),
-        ModelType2.BAIDU_MODEL,
-        lambda x, y, z: baidu_label(x, y, z),
-        lambda x: baidu_process(x)),
-    # 航道模型
-    ModelType2.CHANNEL_EMERGENCY_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.CHANNEL_EMERGENCY_MODEL, t, z, h),
-        ModelType2.CHANNEL_EMERGENCY_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)),
-    # 河道检测模型
-    ModelType2.RIVER2_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.RIVER2_MODEL, t, z, h),
-        ModelType2.RIVER2_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)),
-    # 城管模型
-    ModelType2.CITY_MANGEMENT_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.CITY_MANGEMENT_MODEL, t, z, h),
-        ModelType2.CITY_MANGEMENT_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 人员落水模型
-    ModelType2.DROWING_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.DROWING_MODEL, t, z, h),
-        ModelType2.DROWING_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 城市违章模型
-    ModelType2.NOPARKING_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.NOPARKING_MODEL, t, z, h),
-        ModelType2.NOPARKING_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 城市公路模型
-    ModelType2.CITYROAD_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.CITYROAD_MODEL, t, z, h),
-        ModelType2.CITYROAD_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-    # 加载坑槽模型
-    ModelType2.POTHOLE_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: Model(x, y, r, ModelType2.POTHOLE_MODEL, t, z, h),
-        ModelType2.POTHOLE_MODEL,
-        lambda x, y, z: one_label(x, y, z),
-        lambda x: model_process(x)
-    ),
-}

util/PlotsUtils.py

@@ -24,6 +24,7 @@ def get_label_array(color=None, label=None, font=None, fontSize=40, unify=False)
     im_array = cv2.resize(im_array, (0, 0), fx=scale, fy=scale)
     return im_array
 
+
 def get_label_arrays(labelNames, colors, fontSize=40, fontPath="platech.ttf"):
     font = ImageFont.truetype(fontPath, fontSize, encoding='utf-8')
     label_arraylist = [get_label_array(colors[i % 20], label_name, font, fontSize) for i, label_name in
@@ -49,48 +50,6 @@ def get_label_array_dict(colors, fontSize=40, fontPath="platech.ttf"):
         zh_dict[code] = arr
     return zh_dict
 
-def get_label_left(x0,y1,label_array,img):
-    imh, imw = img.shape[0:2]
-    lh, lw = label_array.shape[0:2]
-    # x1 框框左上x位置 + 描述的宽
-    # y0 框框左上y位置 - 描述的高
-    x1, y0 = x0 + lw, y1 - lh
-    # 如果y0小于0, 说明超过上边框
-    if y0 < 0:
-        y0 = 0
-        # y1等于文字高度
-        y1 = y0 + lh
-    # 如果y1框框的高大于图片高度
-    if y1 > imh:
-        # y1等于图片高度
-        y1 = imh
-        # y0等于y1减去文字高度
-        y0 = y1 - lh
-    # 如果x0小于0
-    if x0 < 0:
-        x0 = 0
-        x1 = x0 + lw
-    if x1 > imw:
-        x1 = imw
-        x0 = x1 - lw
-    return x0,y0,x1,y1
-
-def get_label_right(x1,y0,label_array):
-    lh, lw = label_array.shape[0:2]
-    # x1 框框右上x位置 + 描述的宽
-    # y0 框框右上y位置 - 描述的高
-    x0, y1 = x1 - lw, y0 - lh
-    # 如果y0小于0, 说明超过上边框
-    if y0 < 0 or y1 < 0:
-        y1 = 0
-        # y1等于文字高度
-        y0 = y1 + lh
-    # 如果x0小于0
-    if x0 < 0 or x1 < 0:
-        x0 = 0
-        x1 = x0 + lw
-
-    return x0,y1,x1,y0
 
 def xywh2xyxy(box):
     if not isinstance(box[0], (list, tuple, np.ndarray)):
@@ -116,24 +75,42 @@ def xy2xyxy(box):
         box = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
     return box
 
-def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=None, isNew=False, border=None):
+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
-    # 图片的长度和宽度
-    if border is not None:
-        border = np.array(border,np.int32)
-        color,label_array=draw_name_border(box,color,label_array,border)
-        #img = draw_transparent_red_polygon(img,border,'',alpha=0.1)
-
     lh, lw = label_array.shape[0:2]
-    tl = config[0]
-    if isinstance(box[-1], np.ndarray):
-        return draw_name_points(img,box,color)
-
-    label = ' %.2f' % score
+    # 图片的长度和宽度
+    imh, imw = img.shape[0:2]
     box = xywh2xyxy(box)
     # 框框左上的位置
     x0, y1 = box[0][0], box[0][1]
-    x0, y0, x1, y1 = get_label_left(x0, y1, label_array, img)
+    # if score_location == 'leftTop':
+    #     x0, y1 = box[0][0], box[0][1]
+    # # 框框左下的位置
+    # elif score_location == 'leftBottom':
+    #     x0, y1 = box[3][0], box[3][1]
+    # else:
+    #     x0, y1 = box[0][0], box[0][1]
+    # x1 框框左上x位置 + 描述的宽
+    # y0 框框左上y位置 - 描述的高
+    x1, y0 = x0 + lw, y1 - lh
+    # 如果y0小于0, 说明超过上边框
+    if y0 < 0:
+        y0 = 0
+        # y1等于文字高度
+        y1 = y0 + lh
+    # 如果y1框框的高大于图片高度
+    if y1 > imh:
+        # y1等于图片高度
+        y1 = imh
+        # y0等于y1减去文字高度
+        y0 = y1 - lh
+    # 如果x0小于0
+    if x0 < 0:
+        x0 = 0
+        x1 = x0 + lw
+    if x1 > imw:
+        x1 = imw
+        x0 = x1 - lw
     # 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
@@ -143,12 +120,14 @@ def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=Non
     5. thickness（int）：画线的粗细
     6. shift：顶点坐标中小数的位数
     '''
-    img[y0:y1, x0:x1, :] = label_array
+    tl = config[0]
     box1 = np.asarray(box, np.int32)
     cv2.polylines(img, [box1], True, color, tl)
+    img[y0:y1, x0:x1, :] = label_array
     pts_cls = [(x0, y0), (x1, y1)]
     # 把英文字符score画到类别旁边
     # tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
+    label = ' %.2f' % score
     # tf = max(tl, 1)
     # fontScale = float(format(imw / 1920 * 1.1, '.2f')) or tl * 0.33
     # fontScale = tl * 0.33
@@ -251,6 +230,7 @@ def draw_name_ocr(box, img, color, line_thickness=2, outfontsize=40):
     # (color=None, label=None, font=None, fontSize=40, unify=False)
     label_zh = get_label_array(color, box[0], font, outfontsize)
     return plot_one_box_auto(box[1], img, color, line_thickness, label_zh)
+
 def filterBox(det0, det1, pix_dis):
     # det0为 (m1, 11) 矩阵
     # det1为 (m2, 12) 矩阵
@@ -296,7 +276,6 @@ def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
     # print("省略 :%s, lh:%s, lw:%s"%('+++' * 10, lh, lw))
     # 图片的长度和宽度
     imh, imw = img.shape[0:2]
-    points = None
     box = xy2xyxy(box)
     # 框框左上的位置
     x0, y1 = box[0][0], box[0][1]
@@ -337,22 +316,21 @@ def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
 
     return img, box
 
-def draw_name_crowd(dets, img, color, outfontsize=20):
+
+def draw_name_crowd(dets, img, color, line_thickness=2, outfontsize=20):
     font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
-    if len(dets) == 2:
+    if len(dets) == 1:
         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)
+            img = cv2.circle(img, (int(p[0]), int(p[1])), line_thickness, 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:
+    elif len(dets) == 2:
         detP = dets[1]
-        line = dets[2]
         for p in detP:
-            img = cv2.circle(img, (int(p[0]), int(p[1])), line, color, -1)
+            img = cv2.circle(img, (int(p[0]), int(p[1])), line_thickness, color, -1)
 
         detM = dets[0]
         h, w = img.shape[:2]
@@ -387,90 +365,4 @@ def draw_name_crowd(dets, img, color, outfontsize=20):
             img[y0:y1, x0:x1, :] = label_arr
 
 
-    return img, dets
-
-def draw_name_points(img,box,color):
-    font = ImageFont.truetype(FONT_PATH, 6, encoding='utf-8')
-    points = box[-1]
-    arrea = cv2.contourArea(points)
-    label = '火焰'
-    arealabel = '面积:%s' % f"{arrea:.1e}"
-    label_array_area = get_label_array(color, arealabel, font, 10)
-    label_array = get_label_array(color, label, font, 10)
-    lh_area, lw_area = label_array_area.shape[0:2]
-    box = box[:4]
-    # 框框左上的位置
-    x0, y1 = box[0][0], max(box[0][1] - lh_area - 3, 0)
-    x1, y0 = box[1][0], box[1][1]
-    x0_label, y0_label, x1_label, y1_label = get_label_left(x0, y1, label_array, img)
-    x0_area, y0_area, x1_area, y1_area = get_label_right(x1, y0, label_array_area)
-    img[y0_label:y1_label, x0_label:x1_label, :] = label_array
-    img[y0_area:y1_area, x0_area:x1_area, :] = label_array_area
-    # cv2.drawContours(img, points, -1, color, tl)
-    cv2.polylines(img, [points], False, color, 2)
-    if lw_area < box[1][0] - box[0][0]:
-        box = [(x0, y1), (x1, y1), (x1, box[2][1]), (x0, box[2][1])]
-    else:
-        box = [(x0_label, y1), (x1, y1), (x1, box[2][1]), (x0_label, box[2][1])]
-    box = np.asarray(box, np.int32)
-    cv2.polylines(img, [box], True, color, 2)
-    return img, box
-
-def draw_name_border(box,color,label_array,border):
-    box = xywh2xyxy(box[:4])
-    cx, cy = int((box[0][0] + box[2][0]) / 2), int((box[0][1] + box[2][1]) / 2)
-    flag = cv2.pointPolygonTest(border, (int(cx), int(cy)),
-                                False)  # 若为False，会找点是否在内，外，或轮廓上
-    if flag == 1:
-        color = [0, 0, 255]
-        # 纯白色是(255, 255, 255)，根据容差定义白色范围
-        lower_white = np.array([255 - 30] * 3, dtype=np.uint8)
-        upper_white = np.array([255, 255, 255], dtype=np.uint8)
-        # 创建白色区域的掩码（白色区域为True，非白色为False）
-        white_mask = cv2.inRange(label_array, lower_white, upper_white)
-        # 创建与原图相同大小的目标颜色图像
-        target_img = np.full_like(label_array, color, dtype=np.uint8)
-        # 先将非白色区域设为目标颜色，再将白色区域覆盖回原图颜色
-        label_array = np.where(white_mask[..., None], label_array, target_img)
-    return color,label_array
-
-def draw_transparent_red_polygon(img, points, alpha=0.5):
-    """
-    在图像中指定的多边形区域绘制半透明红色
-
-    参数:
-        image_path: 原始图像路径
-        points: 多边形顶点坐标列表，格式为[(x1,y1), (x2,y2), ..., (xn,yn)]
-        output_path: 输出图像路径
-        alpha: 透明度系数，0-1之间，值越小透明度越高
-    """
-    # 读取原始图像
-    if img is None:
-        raise ValueError(f"无法读取图像")
-
-    # 创建与原图大小相同的透明图层（RGBA格式）
-    overlay = np.zeros((img.shape[0], img.shape[1], 4), dtype=np.uint8)
-
-    # 将点列表转换为适合cv2.fillPoly的格式
-    #pts = np.array(points, np.int32)
-    pts = points.reshape((-1, 1, 2))
-
-    # 在透明图层上绘制红色多边形（BGR为0,0,255）
-    # 最后一个通道是Alpha值，控制透明度，黄色rgb
-    cv2.fillPoly(overlay, [pts], (255, 0, 0, int(alpha * 255)))
-
-    # 将透明图层转换为BGR格式（用于与原图混合）
-    overlay_bgr = cv2.cvtColor(overlay, cv2.COLOR_RGBA2BGR)
-
-    # 创建掩码，用于提取红色区域
-    mask = overlay[:, :, 3] / 255.0
-    mask = np.stack([mask] * 3, axis=-1)  # 转换为3通道
-
-    # 混合原图和透明红色区域
-    img = img * (1 - mask) + overlay_bgr * mask
-    img = img.astype(np.uint8)
-
-    # # 保存结果
-    # cv2.imwrite(output_path, result)
-
-    return img
+    return img, dets

util/TorchUtils.py

@@ -70,7 +70,7 @@ def select_device(device='0'):
     # 设置环境变量
     os.environ['CUDA_VISIBLE_DEVICES'] = device
     assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'
-    return torch.device('cuda:%s' % device)
+    return torch.device('cuda:1')
 
 
 # def select_device(device='', batch_size=None):