update

README.md

@@ -1,8 +1,2 @@
-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。
+1.dsj
-
+2.0811 
-4、2025.07.10 周树亮 - 增加人群计数，自研车牌模型，裸土覆盖3个场景
-
-5、江朝庆 -- 0715
-1）代码整理，删除冗余代码。
-2）增加requirements.txt,方便部署
-3) logs 

concurrency/FileUploadThread.py

@@ -6,21 +6,25 @@ from traceback import format_exc
 
 from loguru import logger
 import cv2
+import requests
+import json
+import base64
 from entity.FeedBack import message_feedback
 from enums.ExceptionEnum import ExceptionType
-from enums.ModelTypeEnum import ModelType
 from exception.CustomerException import ServiceException
 from util.AliyunSdk import AliyunOssSdk
 from util.MinioSdk import MinioSdk
 from util import TimeUtils
 from enums.AnalysisStatusEnum import AnalysisStatus
-from util.PlotsUtils import draw_painting_joint, draw_name_ocr, draw_name_crowd
+from util.PlotsUtils import draw_painting_joint
 from util.QueUtil import put_queue, get_no_block_queue, clear_queue
 import io
 from util.LocationUtils import locate_byMqtt
 
+
+
 class FileUpload(Thread):
-    __slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg','_mqtt_list')
+    __slots__ = ('_fb_queue', '_context', '_image_queue', '_analyse_type', '_msg', '_mqtt_list')
 
     def __init__(self, *args):
         super().__init__()
@@ -33,6 +37,16 @@ class FileUpload(Thread):
         
         
 
+        
+        self.server_ip =  self._context['llm']['server_ip']
+        self.server_port = self._context['llm']['server_port']
+        self.is_use_llm = self._context['llm']['is_use_llm']
+        self.cnt = self._context['llm']['cnt']
+        
+        
+        
+        
+        
         #0521:
         default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"    
         if default_enabled:
@@ -44,15 +58,74 @@ class FileUpload(Thread):
             # 这里放非默认逻辑的代码
             self._algSwitch = False
 
-        print("---line46 :FileUploadThread.py---",self._algSwitch)
+# 如果任务是在线、离线处理，则用此类
-
-
-#如果任务是在线、离线处理，则用此类
 class ImageFileUpload(FileUpload):
     __slots__ = ()
 
+    def extract_answers(self, text):  # 提取大模型中的分点文本内容
+        lines = [line.strip() for line in text.split('.') if line.strip()]
+        answers = []
+        for i, line in enumerate(lines):
+            if i > 0:
+                if i < len(lines) - 1:
+                    answers.append(line[:-1])
+                else:
+                    answers.append(line)
+        return answers
+    
+    def get_llm_res(self, image, prompt,SERVER_IP,API_PORT):
+
+        # SERVER_IP = "192.168.10.11"
+        # API_URL = f"http://{SERVER_IP}:8000/generate"
+
+        SERVER_IP = SERVER_IP
+        
+        API_URL = f"http://{SERVER_IP}:{API_PORT}/generate"
+
+        # _, buffer = cv2.imencode('.jpg', image)
+        # is_success, buffer = cv2.imencode('.png', image)
+        is_success, buffer = cv2.imencode('.webp', image, [cv2.IMWRITE_WEBP_QUALITY, 100])
+        image_base64 = base64.b64encode(buffer).decode('utf-8')
+
+        conversation = {
+            "prompt": prompt,
+            "image_base64": image_base64,
+        }
+
+        try:
+            logger.info(f"正在向 {API_URL} 发送请求...")
+            response = requests.post(API_URL, json=conversation, timeout=60) # 设置超时
+
+            response.raise_for_status() # 如果状态码不是2xx，会抛出异常
+
+            resp = response.json()
+            result = resp.get("response")
+
+            logger.info("\n--- 请求成功 ---")
+            logger.info(f"原始Prompt: {prompt}")
+            logger.info(f"模型生成结果: {result}")
+
+        except requests.exceptions.RequestException as e:
+            logger.info(f"\n--- 请求失败 ---")
+            logger.info(f"发生错误: {e}")
+        except json.JSONDecodeError:
+            logger.info("\n--- 解析失败 ---")
+            logger.info(f"无法解析服务器返回的响应: {response.text}")
+
+        new_result = self.extract_answers(result)
+        print("###line116",new_result)
+        
+        
+        
+        
+        new_result = new_result[1]  # 获取第二点内容
+        # flag = "不" in result or ("没" in result and "没错" not in result) or "否" in result or "未" in result or "无" in result
+        flag = "不存在" in new_result or ("没" in new_result and "没错" not in new_result) or (
+                           "否" in new_result and "是否" not in new_result) or "未" in new_result or "无法" in new_result
+        return not flag, new_result
+
     #@staticmethod
-    def handle_image(self,frame_msg, frame_step):
+    def handle_image(self, frame_msg, frame_step):
         # (high_score_image["code"], all_frames, draw_config["font_config"])
         # high_score_image["code"][code][cls] = (frame, frame_index_list[i], cls_list)
         det_xywh, frame, current_frame, all_frames, font_config = frame_msg
@@ -67,7 +140,6 @@ 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():
@@ -76,27 +148,15 @@ class ImageFileUpload(FileUpload):
                     if len(target_list) > 0:
                         aFrame = frame.copy()
                         for target in target_list:
-                            # 自研车牌模型判断
+                            draw_painting_joint(target[1], aFrame, target[3], target[2], target[4], font_config, target[5])
-                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
+                            igH,igW = aFrame.shape[0:2]
-                                box = [target[1][0][0], target[1][0][1], target[1][3][0], target[1][3][1]]
+                        if len(self._mqtt_list)>=1:
-                                draw_name_ocr(box, aFrame, target[4], target[0])
+                            #camParas = self._mqtt_list[0]['data']
-                                cls = 0
-                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
-                                draw_name_crowd(target[3], aFrame, target[4], cls)
-                                cls = 0
-                            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']
                             camParas = self._mqtt_list[0]
-                            gps = locate_byMqtt(target[1], igW, igH, camParas, outFormat='wgs84')
+                            gps = locate_byMqtt(target[1],igW,igH,camParas,outFormat='wgs84')
                         else:
-                            gps = [None, None]
+                            gps=[None,None]
-                        model_info.append(
+                        model_info.append({"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame,'gps':gps})
-                            {"modelCode": str(code), "detectTargetCode": str(cls), "aFrame": aFrame, 'gps': gps})
         if len(model_info) > 0:
             image_result = {
                 "or_frame": frame,
@@ -153,30 +213,89 @@ class ImageFileUpload(FileUpload):
                                                                      image_result["last_frame"],
                                                                      analyse_type,
                                                                      "OR", "0", "0", request_id)
-                                    if self._storage_source==1: 
+                                    if   self._storage_source==1: 
-                                        or_future = t.submit(minioSdk.put_object, or_image,or_image_name)    
+                                        or_future = t.submit(minioSdk.put_object, or_image, or_image_name)    
                                     else:
                                         or_future = t.submit(aliyunOssSdk.put_object, or_image_name, or_image.tobytes())
                                     task.append(or_future)
                                     model_info_list = image_result["model_info"]
+                                    llm_flag_list = []
                                     msg_list = []
-                                    for model_info in model_info_list:
-                                        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)
+
+                                    for model_info in model_info_list:
+                                        
+                                        fire_flag = model_info.get("modelCode")
+                                        print("###line229",fire_flag)
+                                        
+                                        if fire_flag == "008":
+                                        
+                                            aFrame = model_info["aFrame"]
+                                            # aFrame_rgb = cv2.cvtColor(aFrame, cv2.COLOR_BGR2RGB)
+
+                                            tar_cls = model_info["detectTargetCode"]
+                                            logger.info("目标类别：{}", tar_cls)
+
+                                            # 0715 ： 这块也不对，因为需要具体到目标才可以，否则只看0 1 ，是无法做到判断正确的火情处理，还有大量其他的部分
+                                            
+                                            # is_use_llm = True
+                                            if tar_cls == '0':
+                                                # tar = "明火"
+                                                tar = "2. 判断图中是否有明火。3. 如果有，识别火势的大小，比如：[无情况/小/中/大]"
+                                            elif tar_cls == '1':
+                                                # tar = "某种物质燃烧产生的烟雾"
+                                                tar = "2. 判断图中是否有烟雾。3. 如果有，识别烟雾的颜色，比如：[无烟雾/白/灰白/灰黑]"
+                                            else:
+                                                self.is_use_llm = False
+                                                
+                                            llm_flag = True
+                                            if self.is_use_llm:
+                                                # prompt = f"我看到图中有{tar}，请你判断是否正确。简要回答。"
+                                                prompt = f"分点进行输出：1. 简短描述图像中的场景。{tar}"
+                                                logger.info("检测到目标，大模型提示词：{}", prompt)
+                                                
+                                                
+                                                # llm_flag, llm_res = self.get_llm_res(aFrame, prompt)
+
+                                                
+                                                llm_flag, llm_res = self.get_llm_res(aFrame, prompt,self.server_ip,self.server_port)
+
+                                                llm_flag_list.append(llm_flag)
+                                                logger.info("检测到目标，大模型识别图中是否存在目标：{}, 大模型输出：{}", llm_flag, llm_res)
+
+                                                if llm_flag:
+                                                    logger.info("经大模型筛查，小模型识别正确!!!")
+                                                    # cv2.imwrite(f"/home/thsw2/wei/image_res/{self.cnt}_{tar_cls}_1.jpg", aFrame, [cv2.IMWRITE_JPEG_QUALITY, 80])
+                                                    # self.cnt = self.cnt + 1
+                                                else:
+                                                    logger.info("经大模型筛查，小模型识别错误!!!")
+                                                    # cv2.imwrite(f"/home/thsw2/wei/image_res/{self.cnt}_{tar_cls}_0.jpg", aFrame, [cv2.IMWRITE_JPEG_QUALITY, 80])
+                                                    # self.cnt = self.cnt + 1
+                                        else:
+                                            llm_flag_list.append(llm_flag)
+                                            
+                                            
+
+                                        ai_image = cv2.imencode(".jpg", aFrame)[1]
+                                        ai_image_name = build_image_name(image_result["current_frame"],
+                                                                        image_result["last_frame"],
+                                                                        analyse_type,
+                                                                        "AI",
+                                                                        model_info["modelCode"],
+                                                                        model_info["detectTargetCode"],
+                                                                        request_id)
+                                        if llm_flag:  # 根据大模型结果判定是否上传
+                                            if self._storage_source==1: 
+                                                ai_future = t.submit(minioSdk.put_object, ai_image,
+                                                                ai_image_name)
+                                            else:
+                                                ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
+                                                                ai_image.tobytes())
+                                            
+                                            task.append(ai_future)
+                                    
+                                    # print("###line288",len(task))
+
                                         #msg_list.append(message_feedback(request_id,
                                         #                                 AnalysisStatus.RUNNING.value,
                                         #                                 analyse_type, "", "", "",
@@ -184,21 +303,25 @@ class ImageFileUpload(FileUpload):
                                         #                                 ai_image_name,
                                         #                                 model_info['modelCode'],
                                         #                                 model_info['detectTargetCode']))
-                                    remote_image_list=[]
-                                    for tk in task:
-                                       remote_image_list.append( tk.result())
   
-                                    for ii,model_info in enumerate(model_info_list):
+                                    remote_image_list = []
-                                        msg_list.append(  message_feedback(request_id,
+                                    for tk in task:
-                                                                         AnalysisStatus.RUNNING.value,
+                                        remote_image_list.append( tk.result() )
-                                                                         analyse_type, "", "", "",
+
-                                                                         remote_image_list[0],
+                                    for ii, model_info in enumerate(model_info_list):
-                                                                         remote_image_list[ii+1],
+                                        
-                                                                         model_info['modelCode'],
+                                        if llm_flag_list[ii]:
-                                                                         model_info['detectTargetCode'],
+                                            msg_list.append(  message_feedback(request_id,
-                                                                         longitude=model_info['gps'][0],
+                                                                            AnalysisStatus.RUNNING.value,
-                                                                         latitude=model_info['gps'][1],
+                                                                            analyse_type, "", "", "",
-                                                                         )  )
+                                                                            remote_image_list[0],
+                                                                            remote_image_list[ii+1],
+                                                                            model_info['modelCode'],
+                                                                            model_info['detectTargetCode'],
+                                                                            longitude=model_info['gps'][0],
+                                                                            latitude=model_info['gps'][1],
+                                                                            )  )
+                                            
                                                                         
                                     if (not self._algSwitch)  or  ( self._algStatus and  self._algSwitch):
                                         for msg in msg_list:
@@ -254,20 +377,12 @@ 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
+                            draw_painting_joint(target[1], aiFrame, target[3], target[2], target[4], font_config)
-                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
+                        model_info.append({
-                                draw_name_ocr(target[1], aiFrame, font_config[cls], target[0])
+                            "modelCode": str(code),
-                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
+                            "detectTargetCode": str(cls),
-                                draw_name_crowd(target[1],aiFrame,font_config[cls],target[0])
+                            "frame": aiFrame
-                            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,
@@ -313,7 +428,7 @@ class ImageTypeImageFileUpload(Thread):
                                     ai_image_name = build_image_name(0, 0, analyse_type, "AI", result.get("modelCode"),
                                                                      result.get("type"), request_id)
                                                                      
-                                    if self._storage_source==1:
+                                    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)
@@ -340,7 +455,7 @@ class ImageTypeImageFileUpload(Thread):
                                                                          analyse_type,
                                                                          "OR", "0", "O", request_id)
                                                                          
-                                            if self._storage_source==1: 
+                                            if   self._storage_source==1: 
                                                 or_future = t.submit(minioSdk.put_object, or_image,image_url_0)    
                                             else:
                                                 or_future = t.submit(aliyunOssSdk.put_object, image_url_0,
@@ -357,7 +472,7 @@ class ImageTypeImageFileUpload(Thread):
                                                                              model_info.get("modelCode"),
                                                                              model_info.get("detectTargetCode"),
                                                                              request_id)
-                                            if self._storage_source==1: 
+                                            if   self._storage_source==1: 
                                                 ai_future = t.submit(minioSdk.put_object, ai_image, ai_image_name)    
                                             else:    
                                                 ai_future = t.submit(aliyunOssSdk.put_object, ai_image_name,
@@ -376,7 +491,8 @@ 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
+                                    
+                                #以下代码是为了获取图像上传后，返回的全路径地址  
                                 if det_xywh is None:
                                     msg_list.append(message_feedback(request_id,
                                                                      AnalysisStatus.RUNNING.value,
@@ -409,6 +525,10 @@ class ImageTypeImageFileUpload(Thread):
                                                                                  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/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, xy2xyxy, draw_name_joint, plot_one_box_auto, draw_name_ocr,draw_name_crowd
+from util.PlotsUtils import draw_painting_joint, filterBox, xywh2xyxy2, draw_name_joint
 
 from util.QueUtil import get_no_block_queue, put_queue, clear_queue
 
@@ -50,8 +50,15 @@ 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"]:
@@ -148,39 +155,17 @@ 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
+                                            try:   # 应对NaN情况
-                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
+                                                box, score, cls = xywh2xyxy2(qs)
-                                                cls = 0
+                                            except:
-                                                ocrlabel, xybox = qs
+                                                continue
-                                                box = xy2xyxy(xybox)
+                                            if cls not in allowedList or score < frame_score:
-                                                score = None
+                                                continue
-                                                color = rainbows[cls]
+                                            label_array, color = label_arrays[cls], rainbows[cls]
-                                                label_array = None
+                                            if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
-                                                rr = t.submit(draw_name_ocr, xybox, copy_frame, color, ocrlabel)
+                                                rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
-                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
-                                                cls = 0
-                                                crowdlabel, points = qs
-                                                box = [(0, 0), (0, 0), (0, 0), (0, 0)]
-                                                score = None
-                                                color = rainbows[cls]
-                                                label_array = None
-                                                rr = t.submit(draw_name_crowd, points, copy_frame, color, crowdlabel)
                                             else:
-                                                 try:  # 应对NaN情况
+                                                rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
-                                                     box, score, cls = xywh2xyxy2(qs)
-                                                 except:
-                                                     continue
-                                                 if cls not in allowedList or score < frame_score:
-                                                     continue
-                                                 label_array, color = label_arrays[cls], rainbows[cls]
-                                                 if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
-                                                     rr = t.submit(draw_name_joint, box, copy_frame,
-                                                                   draw_config[code]["label_dict"], score, color,
-                                                                   font_config, qs[6])
-                                                 else:
-                                                     rr = t.submit(draw_painting_joint, box, copy_frame, label_array,
-                                                                   score, color, font_config)
-
                                             thread_p.append(rr)
                                             if det_xywh.get(code) is None:
                                                 det_xywh[code] = {}
@@ -208,9 +193,13 @@ class OnPushStreamProcess(PushStreamProcess):
                                         r.result()
                                 #print('----line173:',self._algSwitch,self._algStatus)        
                                 if self._algSwitch and (not self._algStatus):
-                                    frame_merge = video_conjuncing(frame, frame.copy())
+                                    # frame_merge = video_conjuncing(frame, frame.copy())
+                                    
+                                    frame_merge = frame.copy()
                                 else:    
-                                    frame_merge = video_conjuncing(frame, copy_frame)
+                                    # frame_merge = video_conjuncing(frame, copy_frame)
+                                    
+                                    frame_merge = copy_frame
                                 # 写原视频到本地
                                 write_or_video_result = t.submit(write_or_video, frame, orFilePath, or_video_file,
                                                                  or_write_status, request_id)
@@ -222,7 +211,7 @@ class OnPushStreamProcess(PushStreamProcess):
                                 # 如果有问题, 走下面的逻辑
                                 if qs_np is not None:
                                     if len(qs_np.shape) == 1:
-                                        qs_np = qs_np[np.newaxis,...]
+                                            qs_np = qs_np[np.newaxis,...]
                                     qs_np_id = qs_np.copy()
                                     b = np.ones(qs_np_id.shape[0])
                                     qs_np_id = np.column_stack((qs_np_id,b))
@@ -248,7 +237,7 @@ class OnPushStreamProcess(PushStreamProcess):
                                         if q[11] >= 1:
                                             cls = int(q[9])
                                             if not (cls in new_lab):
-                                                continue  # 为了防止其他类别被带出
+                                                continue        # 为了防止其他类别被带出
                                             code = str(int(q[10])).zfill(3)
                                             if det_xywh2.get(code) is None:
                                                 det_xywh2[code] = {}
@@ -261,11 +250,6 @@ class OnPushStreamProcess(PushStreamProcess):
                                             is_new = False
                                             if q[11] == 1:
                                                 is_new = True
-                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
-                                                cls = ocrlabel
-                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
-                                                cls = crowdlabel
-                                                label_array = points                                            
                                             if cd is None:
                                                 det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]] 
                                             else:
@@ -390,36 +374,15 @@ 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:
-                                            # 自研车牌模型处理
+                                            box, score, cls = xywh2xyxy2(qs)
-                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
-                                                cls = 0
-                                                ocrlabel, xybox = qs
-                                                box = xy2xyxy(xybox)
-                                                score = None
-                                                color = rainbows[cls]
-                                                label_array = None
-                                                label_arrays = [None]
-                                                rr = t.submit(draw_name_ocr,xybox,copy_frame,color,ocrlabel)
-
-                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
-                                                cls = 0
-                                                crowdlabel, points = qs
-                                                box = [(0,0),(0,0),(0,0),(0,0)]
-                                                score = None
-                                                color = rainbows[cls]
-                                                label_array = None
-                                                rr = t.submit(draw_name_crowd, points, copy_frame, color, crowdlabel)
                                       
+                                            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:
-                                                box, score, cls = xywh2xyxy2(qs)
+                                                rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
-                                                if cls not in allowedList or score < frame_score:
-                                                    continue
-                                                label_array, color = label_arrays[cls], rainbows[cls]
-                                                if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
-                                                    rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
-                                                else:
-                                                    rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
-
                                             thread_p.append(rr)
                                  
                                             if det_xywh.get(code) is None:
@@ -447,9 +410,11 @@ class OffPushStreamProcess(PushStreamProcess):
                                     for r in thread_p:
                                         r.result()
                                 if self._algSwitch and (not self._algStatus):
-                                    frame_merge = video_conjuncing(frame, frame.copy())
+                                    # frame_merge = video_conjuncing(frame, frame.copy())
+                                    frame_merge = frame.copy()
                                 else:    
-                                    frame_merge = video_conjuncing(frame, copy_frame)
+                                    # frame_merge = video_conjuncing(frame, copy_frame)
+                                    frame_merge = copy_frame
                                 # 写识别视频到本地
                                 write_ai_video_result = t.submit(write_ai_video, frame_merge, aiFilePath,
                                                                  ai_video_file,
@@ -457,9 +422,10 @@ class OffPushStreamProcess(PushStreamProcess):
                                 push_stream_result = t.submit(push_video_stream, frame_merge, push_p, push_url,
                                                               p_push_status, request_id)
 
+                  
                                 if qs_np is not None:
                                     if len(qs_np.shape) == 1:
-                                        qs_np = qs_np[np.newaxis,...]
+                                            qs_np = qs_np[np.newaxis,...]
                                     qs_np_id = qs_np.copy()
                                     b = np.ones(qs_np_id.shape[0])
                                     qs_np_id = np.column_stack((qs_np_id,b))
@@ -486,7 +452,7 @@ class OffPushStreamProcess(PushStreamProcess):
                                         if q[11] >= 1:
                                             cls = int(q[9])
                                             if not (cls in new_lab):
-                                                continue  # 为了防止其他类别被带出
+                                                continue        # 为了防止其他类别被带出
                                             code = str(int(q[10])).zfill(3)
                                             if det_xywh2.get(code) is None:
                                                 det_xywh2[code] = {}
@@ -499,13 +465,6 @@ class OffPushStreamProcess(PushStreamProcess):
                                             is_new = False
                                             if q[11] == 1:
                                                 is_new = True
-
-                                            if ModelType.CITY_CARPLATE_MODEL.value[1] == str(code):
-                                                cls = ocrlabel
-                                            elif ModelType.CITY_DENSECROWDCOUNT_MODEL.value[1] == str(code):
-                                                cls = crowdlabel
-                                                label_array = points
-
                                             if cd is None:
                                                 det_xywh2[code][cls] = [[cls, box, score, label_array, color, is_new]] 
                                             else:

config/service/dsp_test_service.yml

@@ -25,6 +25,10 @@ service:
     # 2 模型追踪
     model_type: 1
     limit: 3
+    # 新增：大模型识别配置
+    llm:
+      server_ip: "192.168.10.11"
+      api_url: "http://192.168.10.11:8000/generate"
   task:
     # 任务限制5个
     limit: 5
@@ -36,4 +40,3 @@ service:
   mqtt_flag: 0
   #是否启用alg控制功能
   algSwitch: False
-