1)新增M027:建筑物下行人检测及计数 2)密集人群计数模型及自研车牌模型优化 3)分类模型支持pt模型加载（巴中水利）

concurrency/FileUploadThread.py

@@ -8,16 +8,18 @@ from loguru import logger
 import cv2
 from entity.FeedBack import message_feedback
 from enums.ExceptionEnum import ExceptionType
+from enums.ModelTypeEnum import ModelType
 from exception.CustomerException import ServiceException
 from util.AliyunSdk import AliyunOssSdk
 from util.MinioSdk import MinioSdk
 from util import TimeUtils
 from enums.AnalysisStatusEnum import AnalysisStatus
-from util.PlotsUtils import draw_painting_joint
+from util.PlotsUtils import draw_painting_joint, draw_name_ocr, draw_name_crowd
 from util.QueUtil import put_queue, get_no_block_queue, clear_queue
 import io
 from util.LocationUtils import locate_byMqtt
 
+
 class FileUpload(Thread):
     __slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg', '_mqtt_list')
 
@@ -30,8 +32,6 @@ class FileUpload(Thread):
         # self._algStatus = True # 默认关闭
         self._algSwitch = self._context['service']['algSwitch']
 
-        
-        
         # 0521:
         default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
         if default_enabled:
@@ -46,14 +46,6 @@ class FileUpload(Thread):
         print("---line46 :FileUploadThread.py---", self._algSwitch)
 
 
-            
-        
-        
-        
-        
-        
-        
-        
 # 如果任务是在线、离线处理，则用此类
 class ImageFileUpload(FileUpload):
     __slots__ = ()
@@ -74,7 +66,6 @@ class ImageFileUpload(FileUpload):
         '''
         print('*' * 100, ' mqtt_list:', len(self._mqtt_list))
 
-            
         model_info = []
         # 更加模型编码解析数据
         for code, det_list in det_xywh.items():
@@ -83,7 +74,16 @@ class ImageFileUpload(FileUpload):
                     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])
+                            # 自研车牌模型判断
+                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
+                                draw_name_ocr(target[1], aFrame, target[4])
+                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                draw_name_crowd(target[1], aFrame, target[4])
+                            else:
+                                draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config,
+                                                    target[5])
+
                         igH, igW = aFrame.shape[0:2]
                         if len(self._mqtt_list) >= 1:
                             # camParas = self._mqtt_list[0]['data']
@@ -91,7 +91,8 @@ class ImageFileUpload(FileUpload):
                             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})
+                        model_info.append(
+                            {"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame, 'gps': gps})
         if len(model_info) > 0:
             image_result = {
                 "or_frame": frame,
@@ -110,8 +111,10 @@ class ImageFileUpload(FileUpload):
         image_queue, fb_queue, analyse_type = self._image_queue, self._fb_queue, self._analyse_type
         service_timeout = int(service["timeout"])
         frame_step = int(service["filter"]["frame_step"]) + 120
-        if msg['taskType']==0: self._algStatus = False
+        if msg['taskType'] == 0:
-        else: self._algStatus = True
+            self._algStatus = False
+        else:
+            self._algStatus = True
         try:
             with ThreadPoolExecutor(max_workers=2) as t:
                 # 初始化oss客户端
@@ -136,8 +139,10 @@ class ImageFileUpload(FileUpload):
                                 if 'stop' == image_msg[1]:
                                     logger.info("开始停止图片上传线程, requestId:{}", request_id)
                                     break
-                                if 'algStart' == image_msg[1]: self._algStatus = True; logger.info("图片上传线程,执行算法开启命令, requestId:{}", request_id)
+                                if 'algStart' == image_msg[1]: self._algStatus = True; logger.info(
-                                if 'algStop' == image_msg[1]:  self._algStatus = False; logger.info("图片上传线程,执行算法关闭命令, requestId:{}", request_id) 
+                                    "图片上传线程,执行算法开启命令, requestId:{}", request_id)
+                                if 'algStop' == image_msg[1]:  self._algStatus = False; logger.info(
+                                    "图片上传线程,执行算法关闭命令, requestId:{}", request_id)
 
                             if image_msg[0] == 1:
                                 image_result = self.handle_image(image_msg[1], frame_step)
@@ -230,6 +235,7 @@ class ImageTypeImageFileUpload(Thread):
         super().__init__()
         self._fb_queue, self._context, self._msg, self._image_queue, self._analyse_type = args
         self._storage_source = self._context['service']['storage_source']
+
     @staticmethod
     def handle_image(det_xywh, copy_frame, font_config):
         """
@@ -249,12 +255,21 @@ class ImageTypeImageFileUpload(Thread):
                     if target_list is not None and len(target_list) > 0:
                         aiFrame = copy_frame.copy()
                         for target in target_list:
+                            # 自研车牌模型判断
+                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
+                                draw_name_ocr(target, aiFrame, font_config[cls])
+                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or \
+                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                draw_name_crowd(target, aiFrame, font_config[cls])
+                            else:
                                 draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
+
                             model_info.append({
                                 "modelCode": str(code),
                                 "detectTargetCode": str(cls),
                                 "frame": aiFrame
                             })
+
         if len(model_info) > 0:
             image_result = {
                 "or_frame": copy_frame,
@@ -363,7 +378,6 @@ class ImageTypeImageFileUpload(Thread):
                                 for thread_result in task:
                                     remote_url_list.append(thread_result.result())
 
-                                    
                                 # 以下代码是为了获取图像上传后，返回的全路径地址
                                 if det_xywh is None:
                                     msg_list.append(message_feedback(request_id,
@@ -394,13 +408,10 @@ class ImageTypeImageFileUpload(Thread):
                                                                                  image_url,
                                                                                  remote_url_list[ii],
                                                                                  model_info_list[ii].get('modelCode'),
-                                                                                 model_info_list[ii].get('detectTargetCode'),
+                                                                                 model_info_list[ii].get(
+                                                                                     'detectTargetCode'),
                                                                                  analyse_results=result))
 
-                                                                             
-                                                                             
-
-                                
                                 for msg in msg_list:
                                     put_queue(fb_queue, msg, timeout=2, is_ex=False)
                         else:

concurrency/IntelligentRecognitionProcess.py

@@ -93,8 +93,6 @@ class IntelligentRecognitionProcess(Process):
 
 
 
-
-
 class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
     __slots__ = ()
 
@@ -113,7 +111,6 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
         pullProcess.start()
         return pullProcess
 
-   
     def upload_video(self,base_dir, env, request_id, orFilePath, aiFilePath):
         if self._storage_source==1:
             minioSdk = MinioSdk(base_dir, env, request_id)
@@ -124,8 +121,6 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
             upload_video_thread_or = Common(aliyunVodSdk.get_play_url, orFilePath, "or_online_%s" % request_id)
             upload_video_thread_ai = Common(aliyunVodSdk.get_play_url, aiFilePath, "ai_online_%s" % request_id)
 
-        
-        
         upload_video_thread_or.setDaemon(True)
         upload_video_thread_ai.setDaemon(True)
         upload_video_thread_or.start()
@@ -245,7 +240,6 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
             # 启动心跳线程
             hb_thread = self.start_heartbeat(fb_queue, hb_queue, request_id, analyse_type, context)
 
-            
             # print_cpu_status(requestId=request_id,lineNum=inspect.currentframe().f_lineno) #7.0,
             # 加载算法模型
             model_array = get_model(msg, context, analyse_type)
@@ -301,6 +295,16 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                     task_status[0] = 1
                                     for i, model in enumerate(model_array):
                                         model_conf, code = model
+                                        if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
+                                           ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                            if draw_config.get(code) is None:
+                                                draw_config[code] = {}
+                                                draw_config["font_config"] = model_conf[4]
+                                                draw_config[code]["allowedList"] = 0
+                                                draw_config[code]["label_arrays"] = [None]
+                                                draw_config[code]["rainbows"] = model_conf[4]
+                                        else:
                                             model_param = model_conf[1]
                                             # (modeType, model_param, allowedList, names, rainbows)
                                             MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0],
@@ -314,6 +318,8 @@ class OnlineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                                 draw_config[code]["label_arrays"] = model_param['label_arraylist']
                                                 if "label_dict" in model_param:
                                                     draw_config[code]["label_dict"] = model_param['label_dict']
+
+
                                 # 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 = []
@@ -616,6 +622,17 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                     task_status[0] = 1
                                     for i, model in enumerate(model_array):
                                         model_conf, code = model
+                                        if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
+                                           ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                            if draw_config.get(code) is None:
+                                                draw_config[code] = {}
+                                                draw_config["font_config"] = model_conf[4]
+                                                draw_config[code]["allowedList"] = 0
+                                                draw_config[code]["label_arrays"] = [None]
+                                                draw_config[code]["rainbows"] = model_conf[4]
+
+                                        else:
                                             model_param = model_conf[1]
                                             # (modeType, model_param, allowedList, names, rainbows)
                                             MODEL_CONFIG[code][2](frame_list[0].shape[1], frame_list[0].shape[0],
@@ -629,6 +646,8 @@ class OfflineIntelligentRecognitionProcess(IntelligentRecognitionProcess):
                                                 draw_config[code]["label_arrays"] = model_param['label_arraylist']
                                                 if "label_dict" in model_param:
                                                     draw_config[code]["label_dict"] = model_param['label_dict']
+
+
                                 det_array = []
                                 for i, frame in enumerate(frame_list):
                                     det_result = t.submit(self.obj_det, self, model_array, frame, task_status,
@@ -922,6 +941,62 @@ class PhotosIntelligentRecognitionProcess(Process):
             logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id)
             raise e
 
+    #自研车牌模型
+    def carplate_rec(self, imageUrl, mod, image_queue, request_id):
+        try:
+            # model_conf： modeType, allowedList, detpar, ocrmodel, rainbows
+            model_conf, code = mod
+            modeType, device, modelList, detpar, rainbows = model_conf
+            image = url2Array(imageUrl)
+            dets = {code: {}}
+            # param = [image, new_device, model, par, img_type, request_id]
+            # model_conf, frame, device, requestId
+            dataBack = MODEL_CONFIG[code][3]([[modeType, device, modelList, detpar], image, request_id])[0][2]
+            dets[code][0] = dataBack
+            if not dataBack:
+                logger.info("车牌识别为空")
+
+            # for ai_result in dataBack:
+            #     label, box = ai_result
+            #     color = rainbows
+
+            if len(dataBack) > 0:
+                put_queue(image_queue, (1, (dets, imageUrl, image, rainbows, "")), timeout=2, is_ex=False)
+
+        except ServiceException as s:
+            raise s
+        except Exception as e:
+            logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id)
+            raise e
+
+    #密集人群计数
+    def denscrowdcount_rec(self, imageUrl, mod, image_queue, request_id):
+        try:
+            # model_conf： modeType, allowedList, detpar, ocrmodel, rainbows
+            model_conf, code = mod
+            modeType, device, model, postPar, rainbows = model_conf
+            image = url2Array(imageUrl)
+            dets = {code: {}}
+            # param = [image, new_device, model, par, img_type, request_id]
+            # model_conf, frame, device, requestId
+            dataBack = MODEL_CONFIG[code][3]([[modeType, device, model, postPar], image, request_id])[0][2]
+            dets[code][0] = dataBack
+            if not dataBack:
+                logger.info("当前页面无人")
+
+            # for ai_result in dataBack:
+            #     label, box = ai_result
+            #     color = rainbows
+
+            if len(dataBack) > 0:
+                put_queue(image_queue, (1, (dets, imageUrl, image, rainbows, '')), timeout=2, is_ex=False)
+
+        except ServiceException as s:
+            raise s
+        except Exception as e:
+            logger.error("模型分析异常: {}, requestId: {}", format_exc(), request_id)
+            raise e
+
     '''
     # 防疫模型
     '''
@@ -936,6 +1011,26 @@ class PhotosIntelligentRecognitionProcess(Process):
             for r in obj_list:
                 r.result(60)
 
+    # 自研车牌识别模型：
+    def carpalteRec(self, imageUrls, model, image_queue, request_id):
+        with ThreadPoolExecutor(max_workers=2) as t:
+            obj_list = []
+            for imageUrl in imageUrls:
+                obj = t.submit(self.carplate_rec, imageUrl, model, image_queue, request_id)
+                obj_list.append(obj)
+            for r in obj_list:
+                r.result(60)
+
+    # 密集人群计数：CITY_DENSECROWDCOUNT_MODEL
+    def denscrowdcountRec(self, imageUrls, model, image_queue, request_id):
+        with ThreadPoolExecutor(max_workers=2) as t:
+            obj_list = []
+            for imageUrl in imageUrls:
+                obj = t.submit(self.denscrowdcount_rec, imageUrl, model, image_queue, request_id)
+                obj_list.append(obj)
+            for r in obj_list:
+                r.result(60)
+
     def image_recognition(self, imageUrl, mod, image_queue, logo, request_id):
         try:
             model_conf, code = mod
@@ -1168,6 +1263,15 @@ class PhotosIntelligentRecognitionProcess(Process):
                     elif model[1] == ModelType.PLATE_MODEL.value[1]:
                         result = t.submit(self.epidemicPrevention, imageUrls, model, base_dir, env, request_id)
                         task_list.append(result)
+                    # 自研车牌模型
+                    elif model[1] == ModelType.CITY_CARPLATE_MODEL.value[1]:
+                        result = t.submit(self.carpalteRec, imageUrls, model, image_queue, request_id)
+                        task_list.append(result)
+                    # 人群计数模型
+                    elif model[1] == ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] or \
+                            model[1] == ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1]:
+                        result = t.submit(self.denscrowdcountRec, imageUrls, model, image_queue, request_id)
+                        task_list.append(result)
                     else:
                         result = t.submit(self.publicIdentification, imageUrls, model, image_queue, logo, request_id)
                         task_list.append(result)
@@ -1380,9 +1484,6 @@ class ScreenRecordingProcess(Process):
         clear_queue(self._hb_queue)
         clear_queue(self._pull_queue)
 
-
-
- 
     def upload_video(self, base_dir, env, request_id, orFilePath):
         if self._storage_source == 1:
             minioSdk = MinioSdk(base_dir, env, request_id)

concurrency/PushVideoStreamProcess.py

@@ -23,7 +23,7 @@ from util.Cv2Utils import video_conjuncing, write_or_video, write_ai_video, push
 from util.ImageUtils import url2Array, add_water_pic
 from util.LogUtils import init_log
 
-from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, draw_name_joint
+from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, xy2xyxy, draw_name_joint, plot_one_box_auto, draw_name_ocr,draw_name_crowd
 
 from util.QueUtil import get_no_block_queue, put_queue, clear_queue
 
@@ -50,15 +50,8 @@ class PushStreamProcess(Process):
             # 这里放非默认逻辑的代码
             self._algSwitch = False
 
-            
         print("---line53 :PushVideoStreamProcess.py---",self._algSwitch)
 
-           
-        
-        
-        
-                  
-       
     def build_logo_url(self):
         logo = None
         if self._context["video"]["video_add_water"]:
@@ -155,6 +148,23 @@ class OnPushStreamProcess(PushStreamProcess):
                                         font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
                                         rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
                                         for qs in det_result:
+                                            # 自研车牌模型处理
+                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
+                                                cls = 0
+                                                box = xy2xyxy(qs[1])
+                                                score = None
+                                                color = rainbows[cls]
+                                                label_array = None
+                                                rr = t.submit(draw_name_ocr, qs, copy_frame, color)
+                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                                cls = 0
+                                                box = [(0, 0), (0, 0), (0, 0), (0, 0)]
+                                                score = None
+                                                color = rainbows[cls]
+                                                label_array = None
+                                                rr = t.submit(draw_name_crowd, qs, copy_frame, color)
+                                            else:
                                                 try:  # 应对NaN情况
                                                     box, score, cls = xywh2xyxy2(qs)
                                                 except:
@@ -163,9 +173,13 @@ class OnPushStreamProcess(PushStreamProcess):
                                                     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])
+                                                    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)
+                                                    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] = {}
@@ -246,6 +260,10 @@ class OnPushStreamProcess(PushStreamProcess):
                                             is_new = False
                                             if q[11] == 1:
                                                 is_new = True
+                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
+                                                ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                                box = qs
                                             if cd is None:
                                                 det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]]
                                             else:
@@ -370,8 +388,27 @@ class OffPushStreamProcess(PushStreamProcess):
                                         font_config, allowedList = draw_config["font_config"], draw_config[code]["allowedList"]
                                         rainbows, label_arrays = draw_config[code]["rainbows"], draw_config[code]["label_arrays"]
                                         for qs in det_result:
-                                            box, score, cls = xywh2xyxy2(qs)
+                                            # 自研车牌模型处理
+                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
+                                                cls = 0
+                                                box = xy2xyxy(qs[1])
+                                                score = None
+                                                color = rainbows[cls]
+                                                label_array = None
+                                                label_arrays = [None]
+                                                rr = t.submit(draw_name_ocr, qs, copy_frame, color)
 
+                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                                cls = 0
+                                                box = [(0,0),(0,0),(0,0),(0,0)]
+                                                score = None
+                                                color = rainbows[cls]
+                                                label_array = None
+                                                rr = t.submit(draw_name_crowd, qs, copy_frame, color)
+
+                                            else:
+                                                box, score, cls = xywh2xyxy2(qs)
                                                 if cls not in allowedList or score < frame_score:
                                                     continue
                                                 label_array, color = label_arrays[cls], rainbows[cls]
@@ -379,6 +416,7 @@ class OffPushStreamProcess(PushStreamProcess):
                                                     rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
                                                 else:
                                                     rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
+
                                             thread_p.append(rr)
 
                                             if det_xywh.get(code) is None:
@@ -416,7 +454,6 @@ class OffPushStreamProcess(PushStreamProcess):
                                 push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
                                                               p_push_status, request_id)
 
-                  
                                 if qs_np is not None:
                                     if len(qs_np.shape) == 1:
                                         qs_np = qs_np[np.newaxis,...]
@@ -459,6 +496,11 @@ class OffPushStreamProcess(PushStreamProcess):
                                             is_new = False
                                             if q[11] == 1:
                                                 is_new = True
+
+                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code) or \
+                                                ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code) or\
+                                                    ModelType.CITY_UNDERBUILDCOUNT_MODEL.value[1] == str(code):
+                                                box = qs
                                             if cd is None:
                                                 det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]] 
                                             else:

enums/ModelTypeEnum.py

@@ -9,15 +9,14 @@ 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 segutils.trafficUtils import tracfficAccidentMixFunction,mixTraffic_postprocess
 from utilsK.drownUtils import mixDrowing_water_postprocess
 from utilsK.noParkingUtils import mixNoParking_road_postprocess
 from utilsK.illParkingUtils import illParking_postprocess
-from utilsK.spillUtils import mixSpillage_postprocess
-from utilsK.cthcUtils import mixCthc_postprocess
 from utilsK.pannelpostUtils import pannel_post_process
 from stdc import stdcModel
 from yolov5 import yolov5Model
+from p2pNet import p2NnetModel
 from DMPRUtils.jointUtil import  dmpr_yolo_stdc
 from AI import default_mix
 from ocr import ocrModel
@@ -121,7 +120,6 @@ class ModelType(Enum):
     })
 
 
-
     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",
@@ -131,8 +129,7 @@ class ModelType(Enum):
         'seg_nclass': 3,
         'segRegionCnt': 2,
         'segPar': {
-            #'modelSize': (640, 360),
+            'modelSize': (640, 360),
-            'modelSize': (1920, 1080),
             'mean': (0.485, 0.456, 0.406),
             'std': (0.229, 0.224, 0.225),
             'predResize': True,
@@ -141,8 +138,7 @@ class ModelType(Enum):
             'mixFunction': {
                 'function': tracfficAccidentMixFunction,
                 'pars': {
-                    #'modelSize': (640, 360),
+                    'modelSize': (640, 360),
-                    'modelSize': (1920,1080),
                     'RoadArea': 16000,
                     'roadVehicleAngle': 15,
                     'speedRoadVehicleAngleMax': 75,
@@ -361,30 +357,27 @@ class ModelType(Enum):
             "classes": 5,
             "rainbows": COLOR
         },
-        # "../weights/pth/AIlib2/%s/yolov5.pt" % modeType.value[3]
         'Detweights': "../weights/trt/AIlib2/river2/yolov5_%s_fp16.engine" % gpuName,
-        # '../weights/pth/AIlib2/%s/stdc_360X640.pth' % modeType.value[3]
         'Segweights': '../weights/trt/AIlib2/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","占道经营","裸土","未覆盖裸土" ],
+        'labelnames': [ "车辆", "垃圾", "商贩", "违停","占道经营","裸土","未覆盖裸土","违建" ],
         'postProcess':{
                     'function':dmpr_yolo_stdc,
                     'pars':{
-                            'carCls':0 ,'illCls':6,'scaleRatio':0.5,'border':80,
+                            'carCls':0 ,'illCls':7,'scaleRatio':0.5,'border':80,
-                            #车辆","垃圾","商贩","裸土","占道经营","违停"--->
+                             #"车辆","垃圾","商贩","裸土","占道经营","未覆盖裸土","违建"
-                             #"车辆","垃圾","商贩","违停","占道经营","裸土"
+                             # key:实际训练index     value:展示index
-                             'classReindex':{ 0:0,1:1,2:2,3:6,4:4,5:5,6:3}
+                             'classReindex':{ 0:0,1:1,2:2,7:3,4:4,3:5,5:6,6:7}
                         }
                     },
         'models':[
             {
-                'weight':'../weights/pth/AIlib2/cityMangement3/yolov5.pt',
+                 'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
-                #'weight':'../weights/trt/AIlib2/cityMangement3/yolov5_%s_fp16.engine'%(gpuName),
                  'name':'yolov5',
                 'model':yolov5Model,
-                'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.4,"2":0.5,"3":0.5  } }
+                'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3,4,5,6,7],'segRegionCnt':1, 'trtFlag_det':True,'trtFlag_seg':True, "score_byClass":{"0":0.8,"1":0.4,"2":0.5,"3":0.5  } }
             },
             {
                 'weight':'../weights/pth/AIlib2/cityMangement3/dmpr.pth',
@@ -396,9 +389,9 @@ class ModelType(Enum):
                 'name':'dmpr'
             },
             {
-                'weight':'../weights/pth/AIlib2/cityMangement3/stdc_360X640.pth',
+                'weight':'../weights/trt/AIlib2/cityMangement3/stdc_360X640_%s_fp16.engine'%(gpuName),
                 '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
+                    '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'
             }
@@ -407,10 +400,10 @@ class ModelType(Enum):
             "name": "post_process",
             "conf_thres": 0.25,
             "iou_thres": 0.45,
-            "classes": 6,
+            "classes": 8,
             "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
+        '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,
     })
@@ -443,9 +436,7 @@ class ModelType(Enum):
             "classes": 9,
             "rainbows": COLOR
         },
-        # "../weights/pth/AIlib2/%s/yolov5.pt" % modeType.value[3]
         'Detweights': "../weights/trt/AIlib2/drowning/yolov5_%s_fp16.engine" % gpuName,
-        # '../weights/pth/AIlib2/%s/stdc_360X640.pth' % modeType.value[3]
         'Segweights': '../weights/trt/AIlib2/drowning/stdc_360X640_%s_fp16.engine' % gpuName
     })
 
@@ -514,7 +505,7 @@ class ModelType(Enum):
 
     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"],
+        'labelnames': ["护栏", "交通标志", "非交通标志", "施工锥桶", "施工水马"],
         'trtFlag_seg': False,
         'trtFlag_det': True,
         'slopeIndex': [],
@@ -574,14 +565,12 @@ class ModelType(Enum):
         }},
         'models':[
         {
-            #'weight':'../weights/pth/AIlib2/channel2/yolov5.pt',
             'weight':'../weights/trt/AIlib2/channel2/yolov5_%s_fp16.engine'%(gpuName),
             'name':'yolov5',
             'model':yolov5Model,
             'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.7,"1":0.7,"2":0.8,"3":0.6} }
         },
         {
-            # 'weight' : '../weights/trt/AIlib2/ocr2/crnn_ch_4090_fp16_192X32.engine',    
             'weight' : '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
             'name':'ocr',
             'model':ocrModel,
@@ -640,14 +629,10 @@ class ModelType(Enum):
             "classes": 5,
             "rainbows": COLOR
         },
-        # "../weights/pth/AIlib2/%s/yolov5.pt" % modeType.value[3]
         'Detweights': "../weights/trt/AIlib2/riverT/yolov5_%s_fp16.engine" % gpuName,
-        # '../weights/pth/AIlib2/%s/stdc_360X640.pth' % modeType.value[3]
         'Segweights': '../weights/trt/AIlib2/riverT/stdc_360X640_%s_fp16.engine' % gpuName
     })
 
-
-
     FORESTCROWD_FARM_MODEL = ("26", "026", "森林人群模型", 'forestCrowd', lambda device, gpuName: {
         'labelnames': ["林斑", "病死树", "行人", "火焰", "烟雾","人群"],
         'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':5,'distancePersonScale':2.0}},
@@ -678,7 +663,8 @@ class ModelType(Enum):
     })
     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" ],
+        '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,
@@ -694,12 +680,13 @@ class ModelType(Enum):
                 'function': tracfficAccidentMixFunction,
                 'pars': {
                     'modelSize': (640, 360),
-                    #'modelSize': (1920,1080),
                     'RoadArea': 16000,
                     'roadVehicleAngle': 15,
                     'speedRoadVehicleAngleMax': 75,
                     'roundness': 1.0,
                     'cls': 10,
+                    'CarId':1,
+                    'CthcId':1,
                     'vehicleFactor': 0.1,
                     'confThres': 0.25,
                     'roadIou': 0.6,
@@ -785,26 +772,24 @@ class ModelType(Enum):
         'labelnames': ["抛洒物","车辆"],
         'trtFlag_seg': True,
         'trtFlag_det': True,
-        'seg_nclass': 2,
+        'seg_nclass': 3,
         'segRegionCnt': 2,
         'segPar': {
-            #'modelSize': (640, 360),
+            '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': mixSpillage_postprocess,
+                'function': mixTraffic_postprocess,
                 'pars': {
-                    #'modelSize': (640, 360),
+                    'modelSize': (640, 360),
-                    'modelSize': (1920,1080),
                     'RoadArea': 16000,
                     'roadVehicleAngle': 15,
                     'speedRoadVehicleAngleMax': 75,
                     'roundness': 1.0,
-                    'cls': 1,
+                    'cls': 0,
                     'vehicleFactor': 0.1,
                     'confThres': 0.25,
                     'roadIou': 0.6,
@@ -832,26 +817,24 @@ class ModelType(Enum):
         'labelnames': ["危化品","罐体","危险标识","普通车"],
         'trtFlag_seg': True,
         'trtFlag_det': True,
-        'seg_nclass': 2,
+        'seg_nclass': 3,
         'segRegionCnt': 2,
         'segPar': {
-            #'modelSize': (640, 360),
+            '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': mixCthc_postprocess,
+                'function': mixTraffic_postprocess,
                 'pars': {
-                    #'modelSize': (640, 360),
+                    'modelSize': (640, 360),
-                    'modelSize': (1920,1080),
                     'RoadArea': 16000,
                     'roadVehicleAngle': 15,
                     'speedRoadVehicleAngleMax': 75,
                     'roundness': 1.0,
-                    'cls': 4,
+                    'cls': 0,
                     'vehicleFactor': 0.1,
                     'confThres': 0.25,
                     'roadIou': 0.6,
@@ -865,7 +848,7 @@ class ModelType(Enum):
             "name": "post_process",
             "conf_thres": 0.25,
             "iou_thres": 0.25,
-            "classes": 1,
+            "classes": 4,
             "rainbows": COLOR
         },
         'detModelpara': [{"id": str(x), "config": {"k1": "v1", "k2": "v2"}} for x in [0]],
@@ -896,15 +879,26 @@ class ModelType(Enum):
     })
 
     CITY_CARPLATE_MODEL = ("30", "301", "自研车牌检测", 'carplate', lambda device, gpuName: {
+        'labelnames': ["车牌"],
         'device': str(device),
-        'models':{
+        'rainbows': COLOR,
+        'models': [
             {
-                'weights': '../AIlib2/weights/conf/jkm/plate_yolov5s_v3.jit',
+                'trtFlag_det': False,
+                'weight': '../weights/pth/AIlib2/carplate/plate_yolov5s_v3.jit',
+                'name': 'yolov5',
+                'model': yolov5Model,
+                'par': {
+                    'device': 'cuda:0',
+                    'half': False,
                     'conf_thres': 0.4,
                     'iou_thres': 0.45,
                     'nc': 1,
+                    'plate_dilate': (0.5, 0.1)
+                },
             },
             {
+                'trtFlag_ocr': False,
                 'weight': '../weights/pth/AIlib2/ocr2/crnn_ch.pth',
                 'name': 'ocr',
                 'model': ocrModel,
@@ -919,11 +913,10 @@ class ModelType(Enum):
                     'std': [0.5, 0.5, 0.5],
                     'dynamic': False,
                 }
-                },
+            }],
-            }
     })
 
-    CITY_INFRAREDPERSON_MODEL = ("30", "302", "红外行人模型", 'infraredperson', lambda device, gpuName: {
+    CITY_INFRAREDPERSON_MODEL = ("30", "302", "红外行人模型", 'infraredPerson', lambda device, gpuName: {
         'labelnames': ["行人"],
         'postProcess': {'function': default_mix, 'pars': {}},
         'models':
@@ -965,6 +958,86 @@ class ModelType(Enum):
 
     })
 
+    CITY_DENSECROWDCOUNT_MODEL = ("30", "304", "密集人群计数", 'DenseCrowdCount', lambda device, gpuName: {
+        'labelnames': ["人群计数"],
+        'device': str(device),
+        'rainbows': COLOR,
+        'models': [
+            {
+                'trtFlag_det': False,
+                'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth",  ###检测模型路径
+                'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth",  ###检测模型路径
+                'name': 'p2pnet',
+                'model': p2NnetModel,
+                'par': {
+                    'device': 'cuda:0',
+                    'row': 2,
+                    'line': 2,
+                    'point_loss_coef': 0.45,
+                    'conf': 0.50,
+                    'gpu_id': 0,
+                    'eos_coef': '0.5',
+                    'set_cost_class': 1,
+                    'set_cost_point': 0.05,
+                    'backbone': 'vgg16_bn'
+                },
+            }],
+    })
+
+    CITY_DENSECROWDESTIMATION_MODEL = ("30", "305", "密集人群密度估计", 'DenseCrowdEstimation', lambda device, gpuName: {
+        'labelnames': ["密度"],
+        'models':
+            [
+                {
+                    'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth",  ###检测模型路径
+                    'name': 'yolov5',
+                    'model': yolov5Model,
+                    'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.50, 'iou_thres': 0.45,
+                            'allowedList': list(range(20)), 'segRegionCnt': 1, 'trtFlag_det': True,
+                            'trtFlag_seg': False, "score_byClass": {"0": 0.50, "1": 0.3, "2": 0.3, "3": 0.3}},
+                }
+
+            ],
+        'postFile': {
+            "rainbows": COLOR
+        },
+
+    })
+
+    CITY_UNDERBUILDCOUNT_MODEL = ("30", "306", "建筑物下人群计数", 'perUnderBuild', lambda device, gpuName: {
+        'labelnames': ["建筑物下人群"],
+        'device': str(device),
+        'rainbows': COLOR,
+        'models': [
+            {
+                'weight': "../weights/trt/AIlib2/perUnderBuild/yolov5_%s_fp16.engine" % (gpuName),  ###检测模型路径
+                'name': 'yolov5',
+                'model': yolov5Model,
+                'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.25, 'iou_thres': 0.45,
+                        'allowedList': [0,1,2], 'segRegionCnt': 1, 'trtFlag_det': True,
+                        'trtFlag_seg': False, "score_byClass": {"0": 0.25, "1": 0.3, "2": 0.3, "3": 0.3}},
+            },
+            {
+                'trtFlag_det': False,
+                'weight': "../weights/pth/AIlib2/DenseCrowd/SHTechA.pth",  ###检测模型路径
+                'vggweight': "../weights/pth/AIlib2/DenseCrowd/vgg16_bn-6c64b313.pth",  ###检测模型路径
+                'name': 'p2pnet',
+                'model': p2NnetModel,
+                'par': {
+                    'device': 'cuda:0',
+                    'row': 2,
+                    'line': 2,
+                    'point_loss_coef': 0.45,
+                    'conf': 0.50,
+                    'gpu_id': 0,
+                    'eos_coef': '0.5',
+                    'set_cost_class': 1,
+                    'set_cost_point': 0.05,
+                    'backbone': 'vgg16_bn'
+                },
+            }],
+    })
+
     @staticmethod
     def checkCode(code):
         for model in ModelType:

util/ModelUtils.py

@@ -17,7 +17,7 @@ from util.PlotsUtils import get_label_arrays, get_label_array_dict
 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
+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 stdc import stdcModel
 from segutils.segmodel import SegModel
 from models.experimental import attempt_load
@@ -46,8 +46,12 @@ class OneModel:
             new_device = select_device(par.get('device'))
             half = new_device.type != 'cpu'
             Detweights = par['Detweights']
+            if par['trtFlag_det']:
                 with open(Detweights, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.ERROR)) as runtime:
                     model = runtime.deserialize_cuda_engine(f.read())
+            else:
+                model = attempt_load(Detweights, map_location=new_device)  # load FP32 model
+                if half: model.half()
             par['segPar']['seg_nclass'] = par['seg_nclass']
             Segweights = par['Segweights']
             if Segweights:
@@ -354,6 +358,73 @@ def im_process(args):
         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'] ]
+            logger.info("########################加载 plate_yolov5s_v3.jit 成功 ########################, requestId:{}",
+                        requestId)
+            self.model_conf = (modeType, device, modelList, detpar, par['rainbows'])
+        except Exception:
+            logger.error("模型加载异常：{}, requestId:{}", format_exc(), requestId)
+            raise ServiceException(ExceptionType.MODEL_LOADING_EXCEPTION.value[0],
+                                   ExceptionType.MODEL_LOADING_EXCEPTION.value[1])
+
+
+class DENSECROWDCOUNTModel:
+    __slots__ = "model_conf"
+
+    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:
@@ -608,8 +679,8 @@ MODEL_CONFIG = {
     ),
     # 加载交通模型
     ModelType.TRAFFICFORDSJ_FARM_MODEL.value[1]: (
-        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFIC_FARM_MODEL, t, z, h),
+        lambda x, y, r, t, z, h: OneModel(x, y, r, ModelType.TRAFFICFORDSJ_FARM_MODEL, t, z, h),
-        ModelType.TRAFFIC_FARM_MODEL,
+        ModelType.TRAFFICFORDSJ_FARM_MODEL,
         lambda x, y, z: one_label(x, y, z),
         lambda x: model_process(x)
     ),
@@ -657,6 +728,13 @@ MODEL_CONFIG = {
         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),
@@ -671,4 +749,18 @@ MODEL_CONFIG = {
         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)
+    ),
 }

util/PlotsUtils.py

@@ -2,6 +2,7 @@ import cv2
 import numpy as np
 from PIL import Image, ImageDraw, ImageFont
 import unicodedata
+from loguru import logger
 FONT_PATH = "../AIlib2/conf/platech.ttf"
 
 zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8") 
@@ -67,6 +68,12 @@ def xywh2xyxy2(param):
         # return [(lt, yt), (rt, yt), (rt, yr), (lt, yr)]
     return np.asarray(param[0][0:4], np.int32), float(param[1]), int(param[2])
 
+def xy2xyxy(box):
+    if not isinstance(box[0], (list, tuple, np.ndarray)):
+        x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
+        # 顺时针
+        box = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
+    return box
 
 def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=None, isNew=False):
     # 识别问题描述图片的高、宽
@@ -218,6 +225,11 @@ def draw_name_joint(box, img, label_array_dict, score=0.5, color=None, config=No
     cv2.putText(img, label, p3, 0, config[3], [225, 255, 255], thickness=config[4], lineType=cv2.LINE_AA)
     return img, box
 
+def draw_name_ocr(box, img, color, line_thickness=2, outfontsize=40):
+    font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
+    # (color=None, label=None, font=None, fontSize=40, unify=False)
+    label_zh = get_label_array(color, box[0], font, outfontsize)
+    return plot_one_box_auto(box[1], img, color, line_thickness, label_zh)
 
 def filterBox(det0, det1, pix_dis):
     # det0为 (m1, 11) 矩阵
@@ -256,3 +268,100 @@ def filterBox(det0, det1, pix_dis):
     res = np.sum(mask, axis=1)
     det0_copy[..., -1] = res
     return det0_copy
+
+def plot_one_box_auto(box, img, color=None, line_thickness=2, label_array=None):
+    # print("省略 :%s, box:%s"%('+++' * 10, box))
+    # 识别问题描述图片的高、宽
+    lh, lw = label_array.shape[0:2]
+    # print("省略 :%s, lh:%s, lw:%s"%('+++' * 10, lh, lw))
+    # 图片的长度和宽度
+    imh, imw = img.shape[0:2]
+    box = xy2xyxy(box)
+    # 框框左上的位置
+    x0, y1 = box[0][0], box[0][1]
+    # print("省略 :%s, x0:%s, y1:%s"%('+++' * 10, x0, y1))
+    x1, y0 = x0 + lw, y1 - lh
+    # 如果y0小于0, 说明超过上边框
+    if y0 < 0:
+        y0 = 0
+        # y1等于文字高度
+        y1 = y0 + lh
+    # 如果y1框框的高大于图片高度
+    if y1 > imh:
+        # y1等于图片高度
+        y1 = imh
+        # y0等于y1减去文字高度
+        y0 = y1 - lh
+    # 如果x0小于0
+    if x0 < 0:
+        x0 = 0
+        x1 = x0 + lw
+    if x1 > imw:
+        x1 = imw
+        x0 = x1 - lw
+    # box_tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
+    '''
+    1. img（array） 为ndarray类型（可以为cv.imread）直接读取的数据
+    2. box（array）：为所画多边形的顶点坐标
+    3. 所画四边形是否闭合，通常为True
+    4. color（tuple）：BGR三个通道的值
+    5. thickness（int）：画线的粗细
+    6. shift：顶点坐标中小数的位数
+    '''
+    # Plots one bounding box on image img
+    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
+    box1 = np.asarray(box, np.int32)
+    cv2.polylines(img, [box1], True, color, tl)
+    img[y0:y1, x0:x1, :] = label_array
+
+    return img, box
+
+
+def draw_name_crowd(dets, img, color, line_thickness=2, outfontsize=20):
+    font = ImageFont.truetype(FONT_PATH, outfontsize, encoding='utf-8')
+    if len(dets) == 1:
+        label = '当前人数：%d'%len(dets[0])
+        detP = dets[0]
+        for p in detP:
+            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) == 2:
+        detP = dets[1]
+        for p in detP:
+            img = cv2.circle(img, (int(p[0]), int(p[1])), line_thickness, color, -1)
+
+        detM = dets[0]
+        h, w = img.shape[:2]
+        for b in detM:
+            label = '该建筑下行人及数量：%d'%(int(b[4]))
+            label_arr = get_label_array(color, label, font, outfontsize)
+            lh, lw = label_arr.shape[0:2]
+            # 框框左上的位置
+            x0, y1 = int(b[0]), int(b[1])
+            # print("省略 :%s, x0:%s, y1:%s"%('+++' * 10, x0, y1))
+            x1, y0 = x0 + lw, y1 - lh
+            # 如果y0小于0, 说明超过上边框
+            if y0 < 0:
+                y0 = 0
+                # y1等于文字高度
+                y1 = y0 + lh
+            # 如果y1框框的高大于图片高度
+            if y1 > h:
+                # y1等于图片高度
+                y1 = h
+                # y0等于y1减去文字高度
+                y0 = y1 - lh
+            # 如果x0小于0
+            if x0 < 0:
+                x0 = 0
+                x1 = x0 + lw
+            if x1 > w:
+                x1 = w
+                x0 = x1 - lw
+
+            cv2.polylines(img, [np.asarray(xy2xyxy(b), np.int32)], True, (0, 128, 255), 2)
+            img[y0:y1, x0:x1, :] = label_arr
+
+    return img, dets